Solid-state battery monitoring method and device, electronic equipment and storage medium

Abstract

The invention provides a solid-state battery monitoring method and device, electronic equipment and a storage medium. The method comprises the following steps: extracting a theoretical impedance related parameter corresponding to a current state of a solid-state battery from a mapping table used for representing a mapping relationship between the state of the solid-state battery and the theoretical impedance related parameter of the solid-state battery; based on the theoretical impedance related parameters corresponding to the current state, determining theoretical impedance corresponding to each sampling frequency of the solid-state battery; based on the first impedance spectrum data, determining an impedance weight corresponding to each sampling frequency of the solid-state battery; determining impedance related parameters of the solid-state battery based on the impedance weight corresponding to each sampling frequency, the impedance corresponding to each sampling frequency and the theoretical impedance corresponding to each sampling frequency of the solid-state battery; comparing the impedance related parameters with theoretical impedance related parameters corresponding to the current state; and determining whether the solid-state battery is abnormal or not according to a comparison result. By adopting the technical scheme of the invention, real-time diagnosis of the solid-state battery is realized.

Description

Technical Field

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

[0002] Solid-state batteries, with their non-flammable electrolytes and high energy density potential, are considered a crucial direction for the development of next-generation energy storage technologies and have become a focus of research and industrialization globally. However, solid-state batteries still face a series of challenges in practical applications. Among these, the sudden changes in impedance at the solid electrode / electrolyte interface and interface contact failure are particularly prominent, becoming core technical difficulties restricting their large-scale application. Battery monitoring typically employs EIS (Electromagnetic Insulation) detection, DC monitoring, and NMR (Nuclear Magnetic Resonance) methods.

[0003] However, EIS (Electromagnetic Insulation) testing, DC monitoring, and NMR (Nuclear Magnetic Resonance) methods are all difficult to integrate efficiently with battery management systems at the engineering level, and cannot achieve online, non-destructive, and real-time identification of impedance and degradation issues at the solid-state battery interface during actual vehicle operation. Furthermore, these methods are mostly limited to a single technical approach, and generally suffer from weak noise immunity and insufficient dynamic measurement accuracy under complex automotive conditions. Therefore, how to monitor the solid-state battery interface has become an urgent problem to be solved. Summary of the Invention

[0004] In view of this, embodiments of this application provide a monitoring method, device, electronic device, and storage medium for solid-state batteries, which realizes real-time diagnosis of solid-state batteries, can provide early and accurate warnings of solid-state battery interface failures, improves battery safety, reduces system cost, complexity, and integration difficulty, and improves the anti-interference capability and reliability of monitoring under complex operating conditions.

[0005] This application mainly includes the following aspects: In a first aspect, embodiments of this application provide a method for monitoring solid-state batteries, the monitoring method comprising: Acquire the first impedance spectrum data of the solid-state battery; wherein, the first impedance spectrum data includes the impedance of the solid-state battery at multiple sampling frequencies; The theoretical impedance-related parameters of the solid-state battery in its current state are extracted from the mapping table that characterizes the state of the solid-state battery and the theoretical impedance-related parameters of the solid-state battery. Based on the theoretical impedance-related parameters corresponding to the current state, the theoretical impedance of the solid-state battery at each sampling frequency is determined. Based on the first impedance spectrum data, determine the impedance weight of the solid-state battery at each sampling frequency; Based on the impedance weight of the solid-state battery at each sampling frequency, the impedance of the solid-state battery at each sampling frequency, and the theoretical impedance of the solid-state battery at each sampling frequency, the impedance-related parameters of the solid-state battery are determined. Compare the impedance-related parameters with the theoretical impedance-related parameters corresponding to the current state; When the comparison result between the impedance-related parameters and the theoretical impedance-related parameters corresponding to the current state meets the first preset fault condition, it is determined that the solid-state battery has malfunctioned.

[0006] Furthermore, the mapping table is constructed in the following way: For each test state of the solid-state battery in multiple test states, when the solid-state battery is in that test state, the second impedance spectrum data of the solid-state battery is acquired; Based on the second impedance spectrum data, the theoretical impedance-related parameters of the solid-state battery in this test state are determined. The mapping table is constructed based on the theoretical impedance parameters of the solid-state battery under all test conditions.

[0007] Furthermore, the second impedance spectrum data includes: a Nyquist curve characterizing the relationship between the real and imaginary parts of the solid-state battery impedance, and the phase of the solid-state battery at multiple test frequencies; the determination of the theoretical impedance-related parameters of the solid-state battery in this test state based on the second impedance spectrum data includes: For each test state, determine the preset impedance frequency band and preset diffusion impedance frequency band corresponding to that test state; Based on the Nyquist curve, the initial impedance-related parameters corresponding to each impedance band of the preset impedance frequency band are determined; for each impedance band, the initial impedance-related parameters corresponding to the impedance band are iterated based on the impedance corresponding to the impedance band. Based on the type of Nyquist curve corresponding to the preset diffusion impedance frequency band, the diffusion impedance of the solid-state battery is determined; based on the diffusion impedance, the initial impedance-related parameters corresponding to the preset diffusion impedance frequency band are iterated. The initial impedance-related parameters corresponding to the preset impedance frequency band and the initial impedance-related parameters corresponding to the preset diffusion impedance frequency band are determined as the theoretical impedance-related parameters of the solid-state battery.

[0008] Furthermore, determining the initial impedance-related parameters corresponding to each impedance frequency band of the preset impedance frequency band based on the Nyquist curve includes: For each impedance frequency band of the preset impedance frequency band, if the Nyquist curve corresponding to the impedance frequency band has a peak, then the test frequency corresponding to the peak is determined as the center frequency corresponding to the impedance frequency band. If the Nyquist curve corresponding to the impedance band does not have a peak, then the lower limit frequency of the impedance band is determined as the center frequency corresponding to the impedance band. Based on the center frequency corresponding to the impedance band, determine the initial impedance-related parameters corresponding to the impedance band.

[0009] Furthermore, the first impedance spectrum data further includes: the phase of the solid-state battery at multiple sampling frequencies and the amplitude of the solid-state battery at multiple sampling frequencies; the step of determining the impedance weight of the solid-state battery at each sampling frequency based on the first impedance spectrum data includes: For each sampling frequency, the phase standard deviation corresponding to that sampling frequency is determined based on the phase corresponding to that sampling frequency; and the first weight corresponding to that sampling frequency is determined based on the phase standard deviation. Based on the amplitude corresponding to the sampling frequency, determine the standard deviation of the impedance amplitude corresponding to the sampling frequency and the mean of the impedance amplitude corresponding to the sampling frequency; based on the standard deviation of the impedance amplitude and the mean, determine the second weight corresponding to the sampling frequency; Based on the energy corresponding to the sampling frequency and the total energy of the sidelobe harmonics corresponding to the sampling frequency, the third weight corresponding to the sampling frequency is determined. Based on the rate of change of the real part and the rate of change of the imaginary part of the impedance corresponding to the sampling frequency, the fourth weight corresponding to the sampling frequency is determined. The result of multiplying the first, second, third, and fourth weights corresponding to each sampling frequency is determined as the impedance weight of the solid-state battery at each sampling frequency.

[0010] Furthermore, before acquiring the first impedance spectrum data of the solid-state battery, the monitoring method further includes: Based on the vehicle's operating status, determine the preset frequency band corresponding to the vehicle's operating status; A preset frequency band excitation current is injected into the solid-state battery to obtain the first impedance spectrum data of the solid-state battery.

[0011] Furthermore, the impedance-related parameters include: bulk impedance and grain boundary impedance; After determining that the solid-state battery has malfunctioned when the comparison result between the impedance-related parameters and the theoretical impedance-related parameters corresponding to the current state meets the first preset fault condition, the monitoring method further includes: The temperature-sensitive slope of the solid-state battery is determined based on the bulk impedance, the grain boundary impedance, and the absolute temperature of the solid-state battery. When the temperature-sensitive slope meets the second preset fault condition, it is determined that the solid-state battery has malfunctioned.

[0012] Secondly, embodiments of this application also provide a monitoring device for solid-state batteries, the monitoring device comprising: The acquisition module is used to acquire the first impedance spectrum data of the solid-state battery; wherein, the first impedance spectrum data includes the impedance of the solid-state battery at multiple sampling frequencies; The extraction module is used to extract the theoretical impedance-related parameters of the solid-state battery in its current state from the mapping table that characterizes the state of the solid-state battery and the theoretical impedance-related parameters of the solid-state battery. The theoretical impedance determination module is used to determine the theoretical impedance of the solid-state battery at each sampling frequency based on the theoretical impedance-related parameters corresponding to the current state. The weight determination module is used to determine the impedance weight of the solid-state battery at each sampling frequency based on the first impedance spectrum data. The parameter determination module is used to determine the impedance-related parameters of the solid-state battery based on the impedance weight of the solid-state battery at each sampling frequency, the impedance of the solid-state battery at each sampling frequency, and the theoretical impedance of the solid-state battery at each sampling frequency. The comparison module is used to compare the impedance-related parameters with the theoretical impedance-related parameters corresponding to the current state; The first anomaly detection module is used to determine that the solid-state battery has an anomaly when the comparison result between the impedance-related parameters and the theoretical impedance-related parameters corresponding to the current state meets the first preset fault condition.

[0013] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. The machine-readable instructions are executed by the processor to perform the steps of the solid-state battery monitoring method described in the first aspect or any possible implementation of the first aspect.

[0014] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the solid-state battery monitoring method described in the first aspect or any possible implementation of the first aspect.

[0015] This application provides a method, apparatus, electronic device, and storage medium for monitoring solid-state batteries. The method extracts the theoretical impedance-related parameters corresponding to the current state of the solid-state battery from a mapping table characterizing the relationship between the solid-state battery's state and its theoretical impedance-related parameters. Based on these parameters, the method determines the theoretical impedance of the solid-state battery at each sampling frequency. Based on first impedance spectrum data, the method determines the impedance weight of the solid-state battery at each sampling frequency. Based on the impedance weight, the impedance, and the theoretical impedance at each sampling frequency, the method determines the impedance-related parameters of the solid-state battery. The method compares these impedance-related parameters with the theoretical impedance-related parameters corresponding to the current state. Based on the comparison results, the method determines whether the solid-state battery is malfunctioning.

[0016] This enables real-time diagnostics of solid-state batteries, providing early and accurate warnings of interface failures and improving battery safety. In addition, it reduces system cost, complexity, and integration difficulty, while enhancing the anti-interference capability and reliability of monitoring under complex operating conditions.

[0017] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of a solid-state battery monitoring method provided in an embodiment of this application is shown; Figure 2 An equivalent circuit topology diagram of a solid-state battery provided in an embodiment of this application is shown; Figure 3 A schematic diagram of the structure of a monitoring device for a solid-state battery provided in an embodiment of this application is shown; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0021] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] The methods, apparatus, electronic devices, or computer-readable storage media described in this application can be applied to any scenario requiring solid-state battery monitoring. This application does not limit specific application scenarios, and any scheme using the solid-state battery monitoring methods and apparatus provided in this application is within the protection scope of this application.

[0023] It is worth noting that solid-state batteries, with their non-flammable electrolyte and high energy density potential, are considered an important development direction for next-generation energy storage technology and have become a focus of research and industrialization globally. However, solid-state batteries still face a series of challenges in practical applications. Among these, the abrupt changes in impedance at the solid electrode / electrolyte interface and interface contact failure are particularly prominent, becoming core technical challenges restricting their large-scale application. Battery monitoring typically employs EIS, DC monitoring, and NMR methods. However, these methods are difficult to integrate efficiently with battery management systems at the engineering level, and cannot achieve online, non-destructive, and real-time identification of impedance and degradation issues at the solid-state battery interface during actual vehicle operation. Furthermore, these methods are often limited to single technical approaches and generally suffer from weak noise immunity and insufficient dynamic measurement accuracy under complex automotive conditions. Therefore, how to monitor the solid-state battery interface has become an urgent problem to be solved.

[0024] To address the aforementioned issues, this application proposes a monitoring method, device, electronic device, and storage medium for solid-state batteries. This enables real-time diagnosis of solid-state batteries, provides early and accurate warnings of interface failures, and improves battery safety. Furthermore, it reduces system cost, complexity, and integration difficulty, while enhancing the anti-interference capability and reliability of monitoring under complex operating conditions.

[0025] To facilitate understanding of this application, the technical solutions provided in this application will be described in detail below with reference to specific embodiments.

[0026] Please see Figure 1 , Figure 1 This is a flowchart illustrating a solid-state battery monitoring method provided in an embodiment of this application.

[0027] like Figure 1 As shown in the figure, the solid-state battery monitoring method provided in this application embodiment includes the following steps: Step S101: Obtain the first impedance spectrum data of the solid-state battery.

[0028] Here, the first impedance spectrum data includes the impedance, phase, and amplitude of the solid-state battery at multiple sampling frequencies. In this step, the impedance spectrum data can be calculated from the voltage and current of the solid-state battery.

[0029] It should be noted that this application performs a complete full-frequency EIS (Electrochemical Impedance Spectroscopy) test during the solid-state battery manufacturing stage to calibrate the solid-state battery and construct a mapping table to characterize the mapping relationship between the solid-state battery state and the theoretical impedance-related parameters of the solid-state battery.

[0030] In this embodiment of the application, the mapping table is constructed through the following steps: Step S11: For each test state of the solid-state battery in multiple test states, when the solid-state battery is in that test state, acquire the second impedance spectrum data of the solid-state battery.

[0031] Here, multiple temperatures and multiple states of charge (SOC) of the battery are pre-set. These multiple temperatures and SOCs are orthogonally combined to obtain multiple combinations, and each combination is determined as the test state of the solid-state battery. The second impedance spectrum data includes: the impedance of the solid-state battery at multiple test frequencies, the Nyquist curve characterizing the relationship between the real and imaginary parts of the solid-state battery impedance, and the phase at multiple test frequencies. The test frequency is the sampling frequency during the testing process.

[0032] In this embodiment of the application, the test frequency range is from 104 Hz dropped to 10 -3 Hz, decreasing logarithmically, with the ratio between test frequencies set to 10. 0.01 The potential disturbance is set to no more than 15 millivolts. The sampling period for each test frequency is 10 cycles to ensure data accuracy. For low-frequency tests, the period is increased to improve the signal-to-noise ratio.

[0033] In this embodiment, after determining the temperature and SOC of the solid-state battery during testing, the solid-state battery is fixed in an electrochemical workstation equipped with an EIS module during the EIS test preparation stage. Throughout the test, the temperature is maintained within ±0.2°C of the target temperature, and the SOC is maintained within ±1% of the target SOC. Furthermore, to reduce external electromagnetic interference, a Faraday cage is added to the outside of the test device for shielding. After the solid-state battery reaches the target temperature and target SOC, the open-circuit voltage of the solid-state battery is monitored, and testing begins when the open-circuit voltage drift is less than 100µV / min. In this application, a frequency scan is performed starting from the highest test frequency and gradually decreasing to the lowest test frequency, making the test more robust.

[0034] Step S12: Based on the second impedance spectrum data, determine the theoretical impedance-related parameters of the solid-state battery in this test state.

[0035] Regarding step S12, as an example in specific implementation, it may include the following steps: Step S121: For each test state, determine the preset impedance frequency band and preset diffusion impedance frequency band corresponding to that test state.

[0036] In this embodiment, for each test state, the test frequency range is divided into a preset impedance frequency band and a preset diffusion impedance frequency band. The preset impedance frequency band includes three impedance bands: the solid electrolyte phase impedance band, the grain boundary impedance band, and the electrode surface reaction interface impedance band; the preset diffusion impedance frequency band is specifically the Warburg diffusion effect impedance band. Specifically, the frequency bands are divided as follows: First, the phases corresponding to multiple test frequencies are plotted as phase curves to characterize the relationship between the test frequency and the phase. Then, the phase curves are preprocessed; specifically, the phase curves are expanded on the logf axis, smoothed using Savitzky-Golay filtering, and robustly denoised (median / Huber) is performed on the smoothed phase curves to remove outliers. Then, based on the phase inflection point method, multiple inflection points of the preprocessed phase curve are determined. Subsequently, the test frequencies corresponding to the multiple inflection points, from largest to smallest, are determined as the transition frequencies between the solid electrolyte phase impedance band and the grain boundary impedance band. f b|gbThe cutoff frequency between the solid electrolyte grain boundary impedance band and the electrode surface reaction interface impedance band. f gb|int And the cutoff frequency between the impedance band region at the electrode surface reaction interface and the impedance band region of the Warburg diffusion effect. f int|w Finally, based on the aforementioned transition frequencies, the impedance frequency bands for the solid electrolyte bulk phase, grain boundary, electrode surface reaction interface, and Warburg diffusion effect can be determined. By dividing these frequency bands, clear boundaries are established between them, enabling stable differentiation of the solid electrolyte bulk phase, solid electrolyte grain boundaries, electrode reaction interfaces, and the component parameter identification regions corresponding to the Warburg diffusion effect. This ensures that the solution of impedance-related parameters is performed within the corresponding frequency bands, avoiding parameter aliasing problems that may occur between different relaxation processes. Specifically, the transition frequency between the solid electrolyte bulk phase impedance frequency band and the grain boundary impedance frequency band... f b|gb This is the lower frequency limit of the solid electrolyte phase impedance band, meaning the solid electrolyte phase impedance band is greater than or equal to... f b|gb The cutoff frequency between the solid electrolyte grain boundary impedance band and the electrode surface reaction interface impedance band. f gb|int This is the lower frequency limit of the grain boundary impedance band, meaning the grain boundary impedance band is greater than or equal to... f gb|int and less than f gb|int The cutoff frequency between the impedance band region at the electrode surface reaction interface and the impedance band region of the Warburg diffusion effect. f int|w This is the lower frequency limit of the electrode surface reaction interface impedance band, that is, the electrode surface reaction interface impedance band is greater than or equal to... f int|w and less than f gb|int The Warburg diffusion effect impedance band is less than f int|w .

[0037] It should be noted that the preset impedance frequency band and preset diffusion impedance frequency band of solid-state batteries are different in different states. Therefore, a mapping table was constructed during calibration to characterize the mapping relationship between battery state and cutoff frequency. Under actual working conditions, the preset impedance frequency band and preset diffusion impedance frequency band corresponding to the current state of the battery can be determined according to the mapping table.

[0038] Step S122: Based on the Nyquist curve, determine the initial impedance-related parameters corresponding to each impedance band of the preset impedance frequency band.

[0039] In this embodiment, the phases corresponding to multiple test frequencies are plotted as amplitude curves to characterize the relationship between the test frequency and the amplitude. The phase curves are preprocessed. Specifically, the amplitude curves are expanded on the logf axis and smoothed using Savitzky-Golay filtering. Robust denoising (median / Huber) is then performed on the smoothed amplitude curves to remove outliers. Based on the preprocessed phase and amplitude curves, a Nyquist curve is plotted to characterize the relationship between the real and imaginary parts of the solid-state battery impedance. Multiple inflection points on the Nyquist and phase curves represent the equivalent impedance of the solid-state battery. Figure 2 The circuit topology shown is included. Impedance-related parameters include: ohmic impedance. R s Bulk impedance R b Bulk nonideal capacitance CPE b Relevant parameters Q b and α b Grain boundary impedance R gb Non-ideal capacitance at grain boundaries CPE gb Relevant parameters Q gb and α gb Interfacial impedance R int Non-ideal capacitance at the reaction interface CPE int Relevant parameters Q int and α int Warburg diffusion impedance Z w The diffusion coefficient σ.

[0040] It should be noted that ohmic impedance R s Bulk impedance R b Bulk nonideal capacitance CPE b Relevant parameters Q b and α The solution needs to be obtained in the bulk phase impedance frequency range of the solid electrolyte, including the grain boundary impedance. R gb Non-ideal capacitance at grain boundaries CPE gb Relevant parameters Q gb and αgb The solution needs to be obtained in the grain boundary impedance frequency range to reflect the interfacial impedance. R int Non-ideal capacitance at the reaction interface CPE int Relevant parameters Q int and α int The solution needs to be obtained in the frequency band of the reaction interface on the electrode surface, and the diffusion coefficient σ needs to be obtained in the frequency band of the Warburg diffusion effect impedance.

[0041] Regarding step S122, as an example in specific implementation, it may include the following steps: Step S1221: For each impedance frequency band of the preset impedance frequency band, if the Nyquist curve corresponding to the impedance frequency band has a peak, then the test frequency corresponding to the peak is determined as the center frequency corresponding to the impedance frequency band.

[0042] Here, for the solid electrolyte phase impedance frequency band, if a segment of the Nyquist curve corresponding to the solid electrolyte phase impedance frequency band shows a apex, i.e., a peak value of the impedance on the imaginary axis, then the test frequency corresponding to the peak value appearing in the solid electrolyte phase impedance frequency band is determined as the center frequency f corresponding to the solid electrolyte phase impedance frequency band. c,b .

[0043] For the grain boundary impedance frequency band, if a segment of the Nyquist curve corresponding to the grain boundary impedance frequency band shows a apex, i.e., a peak value of the impedance on the imaginary axis, then the test frequency corresponding to the peak value in the grain boundary impedance frequency band will be determined as the center frequency f of the corresponding grain boundary impedance frequency band. c,gb .

[0044] For the electrode surface reaction interface impedance frequency band, if a segment of the Nyquist curve corresponding to the electrode surface reaction interface impedance frequency band shows a apex, i.e., a peak value of the impedance on the imaginary axis, then the test frequency corresponding to the peak value in the electrode surface reaction interface impedance frequency band will be determined as the center frequency f of the electrode surface reaction interface impedance frequency band. c,int .

[0045] Step S1222: If the Nyquist curve corresponding to the impedance frequency band does not have a peak, then the lower limit frequency of the impedance frequency band is determined as the center frequency corresponding to the impedance frequency band.

[0046] Here, for the solid electrolyte phase impedance frequency band, if a segment of the Nyquist curve corresponding to the solid electrolyte phase impedance frequency band does not show an arc apex, then the cutoff frequency between the solid electrolyte phase impedance frequency band and the grain boundary impedance frequency band is used. f b|gb The center frequency f corresponding to the phase impedance band of the solid electrolyte was determined. c,b .

[0047] For the grain boundary impedance frequency band, if a segment of the Nyquist curve corresponding to the grain boundary impedance frequency band does not show an arc apex, then the cutoff frequency between the solid electrolyte grain boundary impedance frequency band and the electrode surface reaction interface impedance frequency band is determined. f gb|int The center frequency f corresponding to the grain boundary impedance band was determined. c,gb .

[0048] For the impedance frequency band of the electrode surface reaction interface, if a segment of the Nyquist curve corresponding to the impedance frequency band of the electrode surface reaction interface does not show a apex, then the cutoff frequency between the impedance frequency band at the electrode surface reaction interface and the impedance frequency band of the Warburg diffusion effect is used. f int|w The center frequency f corresponding to the frequency band of the electrode surface reaction interface impedance was determined. c,int .

[0049] Step S1223: Based on the center frequency corresponding to the impedance band, determine the initial impedance-related parameters corresponding to the impedance band.

[0050] Here, for the solid electrolyte phase impedance frequency band, firstly, based on the center frequency f corresponding to the solid electrolyte phase impedance frequency band... c,b Determine the bulk impedance R b The initial value, i.e., R b,0 =2(Imag(Z(2πf c,b Then, the impedance calculation formula for a non-ideal capacitor is Z. CPEi =1 / (Q i (jω) αi ), where Q i for CPE constant, α i α is an exponent, with a value range of [0,1]. This exponent reflects how close a non-ideal capacitor is to an ideal capacitor: when α... i When α approaches 1, it indicates that the properties of a non-ideal capacitor are relatively close to those of an ideal capacitor; when α... i When it approaches 0, its properties become more like those of a resistor. Subsequently, in this application, α... b If the initial value is set to 0, then at the center frequency f c,b At that time, Q b initial value Q b,0 =1 / (R S,0 (2πf c,b Among them, R S,0 R is the initial value of the ohmic impedance. S,0 Calculated as follows: at the point of highest test frequency, i.e., 10. 4 In the impedance response measured at Hz, the real part of the impedance is taken as the ohmic impedance R. sThe initial value, i.e., R S,0 =Real(Z(2π×10 4 )).

[0051] For the grain boundary impedance frequency band, firstly, based on the center frequency f corresponding to the grain boundary impedance frequency band... c,gb Determine grain boundary impedance R gb The initial value, i.e., R gb,0 =2(Imag(Z(2πf c,gb Then, in this application, α gb If the initial value is set to 1, then at the center frequency f c,gb At that time, Q gb initial value Qg b,0 =1 / (R gb,0 (2πf c,gb )).

[0052] For the electrode surface reaction interface impedance frequency band, firstly, based on the center frequency f corresponding to the electrode surface reaction interface impedance frequency band... c,int Determine the reaction interface impedance R int The initial value, i.e., R int,0 =2(Imag(Z(2πf c,int Then, in this application, α int If the initial value is set to 1, then at the center frequency f c,gb At that time, Q int initial value Q int,0 =1 / (R int,0 (2πf c,int )).

[0053] Step S123: For each impedance frequency band, based on the impedance corresponding to that impedance frequency band, iterate the initial impedance-related parameters corresponding to that impedance frequency band.

[0054] Here, for the solid electrolyte bulk impedance frequency band, since the impedance is mainly composed of ohmic impedance and solid electrolyte bulk impedance, the impedance calculation formula corresponding to the solid electrolyte bulk impedance frequency band is as shown in formula (1). (1), In this application, a first parameter matrix is ​​defined. Initial value of the first parameter matrix The initial values ​​of the first parameter matrix are iterated using the complex frequency domain least squares method and gradient descent method. The residual matrix r of the complex frequency domain least squares method... i The calculation formula is r i =Z i -Zi,model Among them, Z i It is the impedance corresponding to the test frequency, i.e., the measured value, Z. i,model The impedance is calculated by substituting the test frequency into the impedance calculation formula. The gradient of the gradient descent method. The calculation formula is: Using the residual matrix and gradient The initial values ​​of the first parameter matrix are iterated. The change in the parameter matrix in each iteration is determined as follows: A damping factor λ is set, and the change in the parameter matrix is ​​determined according to the LM update formula. =-(J T J+λI) -1 J T Where J is the Jacobian matrix and I is the identity matrix. If the computational cost is too high, (J T The term J+λI) can also be replaced by its pseudo-inverse. As an example, in the solid electrolyte bulk impedance band, λ=0.05 is set. In each iteration, θ... Hyper，i+1 =θ Hyper，i + 1, among which, 1 represents the change in the initial value of the first parameter matrix in each iteration. When the maximum value of the residual is less than 10... -5 The iteration ends when the number of iterations reaches 500. This method utilizes all impedances corresponding to the solid electrolyte phase impedance frequency band to obtain the initial impedance-related parameters corresponding to the solid electrolyte phase impedance frequency band after iteration. .

[0055] Here, for the grain boundary impedance frequency band, since the impedance mainly consists of ohmic impedance, bulk impedance of the solid electrolyte, and interface impedance, the impedance calculation formula corresponding to the grain boundary impedance frequency band is as shown in formula (2). (2), In this application, a second parameter matrix is ​​defined. Initial value of the second parameter matrix The initial value of the second parameter matrix is ​​iterated using the least squares method in the complex frequency domain and the gradient descent method. As an example, in the grain boundary impedance band, λ = 0.05 is set. In each iteration, θ... High，i+1 =θ High，i + 2, of which, 2 represents the change in the initial value of the second parameter matrix in each iteration. When the maximum value of the residual is less than 10... -5The iteration ends when the number of iterations reaches 500. Here, all impedances corresponding to the grain boundary impedance frequency band are used to obtain the initial impedance-related parameters corresponding to the grain boundary impedance frequency band after iteration. It should be noted that when calculating the impedance corresponding to the electrode surface reaction interface impedance frequency band, the ohmic impedance in formula (2) is... R s Bulk impedance R b Bulk nonideal capacitance CPE b Relevant parameters Q b and α b These are the initial impedance-related parameters corresponding to the frequency band of the solid electrolyte phase impedance after the above iteration. .

[0056] Here, for the electrode surface reaction interface impedance frequency band, since the impedance in the electrode surface reaction interface impedance frequency band is mainly composed of ohmic impedance, solid electrolyte bulk impedance, grain boundary impedance and reaction interface impedance, the impedance calculation formula corresponding to the electrode surface reaction interface impedance frequency band is as shown in formula (3). (3), In this application, a third parameter matrix is ​​defined. Initial value of the third parameter matrix The initial value of the third parameter matrix is ​​iterated using the least squares method in the complex frequency domain and the gradient descent method. As an example, λ = 0.05 is set in the impedance band of the electrode surface reaction interface. In each iteration, θ... middle，i+1 =θ middle，i + 3, of which, 3 represents the change in the initial value of the third parameter matrix in each iteration. When the maximum value of the residual is less than 10... -5 The iteration ends when the number of iterations reaches 500. Here, all impedances corresponding to the frequency band of the electrode surface reaction interface are used to obtain the initial impedance-related parameters corresponding to the frequency band of the electrode surface reaction interface after iteration. It should be noted that when calculating the impedance corresponding to the frequency band of the electrode surface reaction interface, the ohmic impedance in formula (3) is... R s Bulk impedance R b Bulk nonideal capacitance CPE b Relevant parameters Q b and α b The initial impedance-related parameters corresponding to the phase impedance frequency band of the solid electrolyte after the above iteration, and the grain boundary impedance. Rgb Non-ideal capacitance at grain boundaries CPE gb Relevant parameters Q gb and α gb These are the initial impedance-related parameters corresponding to the grain boundary impedance frequency band after the above iteration. .

[0057] Step S124: Determine the diffusion impedance of the solid-state battery based on the type of Nyquist curve corresponding to the preset diffusion impedance frequency band.

[0058] Here, if the Nyquist curve corresponding to the preset diffusion impedance frequency band is a straight line pointing upwards at 45 degrees, the type is semi-infinite diffusion; if the Nyquist curve corresponding to the preset diffusion impedance frequency band has a bend at the end, the type is finite-length diffusion.

[0059] Specifically, this step begins by determining the cutoff frequency between the impedance band region at the electrode surface reaction interface and the impedance band region of the Warburg diffusion effect. f int|w Determine the relaxation time , =1 / (2π f int|w Then, if the Nyquist curve type corresponding to the preset diffusion impedance frequency band is semi-infinite diffusion, the formula for calculating the Warburg impedance is: If the Nyquist curve type corresponding to the preset diffusion impedance frequency band is finite-length diffusion, then the formula for calculating the Warburg impedance is: Since the impedance in the preset diffusion impedance frequency band consists of ohmic impedance, bulk impedance of solid electrolyte, grain boundary impedance, reaction interface impedance and Warburg impedance, the impedance calculation formula corresponding to the reaction interface impedance frequency band of the electrode surface is as shown in formula (4). (4), In this application, the initial value of the diffusion coefficient σ is set to 10. -3 The diffusion coefficient σ is iteratively calculated using the least squares method in the complex frequency domain and the gradient descent method. As an example, λ=0.1 is set in a preset diffusion impedance frequency band. In each iteration, σ... i+1 =σ i + 4 of which, 4 represents the initial value of the diffusion coefficient σ and its change in each iteration. When the maximum value of the residual is less than 10... -5The iteration ends when the number of iterations reaches 1000. Here, all impedances corresponding to the preset diffusion impedance frequency band are used to obtain the initial impedance-related parameters corresponding to the preset diffusion impedance frequency band after iteration. It should be noted that when calculating the impedance corresponding to the electrode surface reaction interface impedance frequency band, the ohmic impedance in formula (3) is... R s Bulk impedance R b Bulk nonideal capacitance CPE b Relevant parameters Q b and α b The initial impedance-related parameters corresponding to the phase impedance frequency band of the solid electrolyte after the above iteration, and the grain boundary impedance. R gb Non-ideal capacitance at grain boundaries CPE gb Relevant parameters Q gb and α gb α The initial impedance-related parameters corresponding to the grain boundary impedance frequency band after the above iteration reflect the interface impedance. R int Non-ideal capacitance at the reaction interface CPE int Relevant parameters Q int and α in These are the initial impedance-related parameters corresponding to the frequency band of the electrode surface reaction interface impedance after the above iteration.

[0060] Step S124: The initial impedance-related parameters corresponding to the preset impedance frequency band and the initial impedance-related parameters corresponding to the preset diffusion impedance frequency band are determined as the theoretical impedance-related parameters of the solid-state battery.

[0061] Return to reference Figure 2 Step S13: Construct the mapping table based on the theoretical impedance-related parameters of the solid-state battery in all test states.

[0062] Specifically, for each parameter in the impedance-related parameters, when fitting based on temperature changes, the Arrhenius equation is used to fit the temperature dimension. The specific expression is as follows: Where x represents the parameters to be fitted in the temperature dimension, E a,xx0 and x are the temperature-related parameters obtained during the fitting process, and T is the absolute temperature. For each parameter in the impedance-related parameters, when fitting based on the SOC change, a polynomial method is usually used to fit the SOC dimension, preferably a third-order polynomial (from third to fifth order is acceptable), and the specific expression is as follows: Where n is the order of the polynomial, A n denoted as the coefficients of a polynomial of order n.

[0063] In this embodiment, independent fitting is performed based on temperature and SOC respectively. To combine these two fitting equations, the Arrhenius equation can be embedded into the coefficients of each order of the polynomial, specifically expressed as follows: Ultimately, a mapping table can be constructed to characterize the relationship between the state of a solid-state battery and its theoretical impedance-related parameters.

[0064] In this embodiment, all impedance-related parameters are calibrated for different temperature and SOC conditions and bound to specific solid-state battery models. These impedance-related parameters are fitted based on the Arrhenius equation and polynomials to form a complete two-dimensional parameter mapping scale covering different temperature and SOC conditions. Thus, during vehicle operation, the actual impedance can be obtained through real-time online electrochemical impedance spectroscopy (EIS) measurements at a low sampling frequency, providing a benchmark for real-time impedance detection in vehicles and enabling rapid diagnosis of potential battery problems, thereby allowing for the development of corresponding diagnostic and control strategies.

[0065] In this embodiment of the application, before step S101, the monitoring method for the solid-state battery further includes: Step S21: Determine the preset frequency band corresponding to the vehicle's operating status based on the vehicle's operating status.

[0066] Here, during vehicle operation, electromagnetic interference from inverter switches, DC-DC converters, and wiring harness coupling occurs. This interference is primarily concentrated at the switching frequency and its harmonics, significantly impacting the baseline drift of the preset diffusion impedance frequency band. In contrast, the electromagnetic environment is relatively quiet when the vehicle is parked, with slow thermal changes, allowing for long-term integration of low frequencies to obtain high-quality data. While parked, the preset diffusion impedance frequency band is primarily monitored. During operation, excitation is input sequentially in a set order to detect the electrode surface reaction interface impedance band, grain boundary impedance band, and solid electrolyte phase impedance band, achieving full-band battery status monitoring.

[0067] Step S22: Inject the excitation current corresponding to the preset frequency band into the solid-state battery to obtain the first impedance spectrum data of the solid-state battery.

[0068] Here, under on-board operation or injection conditions, excitation current is injected into different preset frequency bands via an on-board DC-DC converter. The real-time sampled voltage and current signals are then bandpass filtered and orthogonally full-pass processed to construct the corresponding dq components. The phasor ratio of voltage and current is calculated in real time to obtain the amplitude and phase of the impedance. In this application, the excitation current is a small-signal current. This process, by acquiring the phasors of voltage and current in real time and calculating the impedance, allows for parallel monitoring with the vehicle's power supply system, thereby optimizing system performance and monitoring efficiency.

[0069] In this embodiment, a periodic reverse charge-discharge current excitation method is used in the preset diffusion impedance frequency band. For the preset impedance frequency band, synchronous detection technology can effectively perform bandpass rejection and optimize signal detection. In the preset impedance frequency band, a small signal current is injected into the DCDC discharge excitation system via the vehicle's DCDC converter without interrupting the existing high and low voltage power supply system. When there is no detection requirement, the DCDC converter will maintain its normal on / off state.

[0070] In this embodiment, to improve detection efficiency and online availability, it is first necessary to preset a set of sampling frequencies for each frequency band based on the sensitivity of electrochemical impedance spectroscopy (EIS) characteristic values ​​to state information in different frequency bands. Then, a preset number of sampling frequencies are selected from the frequency bands for detection according to the actual situation. At each selected sampling frequency, data for a preset period is collected, and a whole-cycle window or exponentially weighted moving average (EWMA) strategy is used to suppress transient disturbances and enhance synchronization and anti-interference capabilities. The specific operation steps are as follows: First, the current temperature T and SOC of the solid battery are read, and the boundary frequencies of the four frequency bands are determined according to the mapping table provided by the manufacturer to characterize the mapping relationship between battery state and transition frequency. Using the boundary frequencies as dividing lines, 3 to 6 sampling frequencies are randomly selected from the sampling frequency set of each frequency band for sampling based on these dividing lines. Different emphases are given to sampling in different frequency domains according to the actual operating state. During operation, parameters from low frequency band to high frequency band are collected in turn, with one frequency band being the main sampled in each round, and the frequency band is changed after one round is completed. Within each cycle of a frequency band, 3 to 6 sampling frequencies are selected within the band range according to a rotation strategy. Each sampling frequency collects N positive cycles (N ranges from 2 to 5) in that cycle. When switching between cycles, the sampling frequency is fine-tuned within each subset as needed until the focus of the collection area changes or the frequency band cycle is changed. The aim is to obtain impedance at several frequencies at low cost without affecting the overall vehicle power supply.

[0071] Return to reference Figure 1 Step S102: Extract the theoretical impedance-related parameters of the solid-state battery in the current state from the mapping table used to characterize the mapping relationship between the solid-state battery state and the theoretical impedance-related parameters of the solid-state battery.

[0072] Here, based on the mapping table used at the time of manufacture to characterize the state of the solid-state battery and the mapping relationship between the theoretical impedance-related parameters of the solid-state battery, the theoretical impedance-related parameters corresponding to the current state can be determined.

[0073] Step S103: Based on the theoretical impedance-related parameters corresponding to the current state, determine the theoretical impedance of the solid-state battery at each sampling frequency.

[0074] Here, based on the theoretical impedance parameters corresponding to the current state, the theoretical impedance of the solid-state battery at each sampling frequency in different frequency bands can be obtained through formulas (1)-(4).

[0075] Step S104: Based on the first impedance spectrum data, determine the impedance weight of the solid-state battery at each sampling frequency.

[0076] Here, the purpose of impedance weighting is to perform consistency and robustness checks, discarding or reducing the weight of data points that do not meet the standards.

[0077] Regarding step S104, as an example in specific implementation, it may include the following steps: Step S1041: For each sampling frequency, determine the phase standard deviation corresponding to that sampling frequency based on the phase corresponding to that sampling frequency.

[0078] Here, the phase standard deviation within a preset period for each sampling frequency is calculated.

[0079] Step S1042: Based on the phase standard deviation, determine the first weight corresponding to the sampling frequency.

[0080] In this application, a phase threshold is set. If the absolute value of the difference between the phase threshold and the phase standard deviation corresponding to the sampling frequency is less than 5°, the first weight is 100%; if the absolute value of the difference between the phase threshold and the phase standard deviation corresponding to the sampling frequency is greater than or equal to 5° and less than or equal to 10°, the first weight is 50%; if the absolute value of the difference between the phase threshold and the phase standard deviation corresponding to the sampling frequency is greater than 10°, the first weight is 0%. Here, phase consistency is guaranteed.

[0081] Step S1043: Based on the amplitude corresponding to the sampling frequency, determine the standard deviation of the impedance amplitude corresponding to the sampling frequency and the mean of the impedance amplitude corresponding to the sampling frequency.

[0082] Here, for each sampling frequency, based on the amplitude corresponding to that sampling frequency, the standard deviation of the amplitude and the mean of the impedance amplitude within a preset period of that sampling frequency are determined.

[0083] Step S1044: Based on the standard deviation of the impedance amplitude and the mean, determine the second weight corresponding to the sampling frequency.

[0084] In this application, if the quotient of the standard deviation of the impedance amplitude and the mean of the impedance amplitude is less than 5°, the second weight is 100%; if the quotient of the standard deviation of the impedance amplitude and the mean of the impedance amplitude is greater than or equal to 5° and less than or equal to 10°, the second weight is 50%; and if the quotient of the standard deviation of the impedance amplitude and the mean of the impedance amplitude is greater than 10°, the second weight is 0%. Here, the stability of the amplitude is guaranteed.

[0085] Step S1045: Based on the energy corresponding to the sampling frequency and the total energy of the sidelobe harmonics corresponding to the sampling frequency, determine the third weight corresponding to the sampling frequency.

[0086] Here, the energy of the sampling frequency is first obtained through a phase-locked loop (PLL) or Fast Fourier Transform (FFT) channel. Then, the total energy of the remaining sidelobe harmonics is obtained, and the energy of the sampling frequency is added to the total energy of the remaining sidelobe harmonics. If the quotient of the sum of the total energy of the remaining sidelobe harmonics and the sum is less than 3°, the third weight is 100%; if the quotient is greater than or equal to 3° and less than or equal to 5°, the third weight is 50%; if the quotient is greater than 5°, the third weight is 0%. This process prevents spectral leakage and harmonic contamination.

[0087] Step S1046: Based on the rate of change of the real part of the impedance and the rate of change of the imaginary part of the impedance corresponding to the sampling frequency, determine the fourth weight corresponding to the sampling frequency.

[0088] Here, the fourth weight corresponding to the sampling frequency can be determined based on the rate of change of the real part and the rate of change of the imaginary part of the impedance within a preset period of the sampling frequency. In this application, thresholds for the rate of change of the real part and the rate of change of the imaginary part are set. If the rate of change of the real part of the impedance is less than 1.5 times the standard deviation corresponding to the real axis, or the rate of change of the imaginary part of the impedance is less than 1.5 times the standard deviation corresponding to the real axis, then the third weight is 50% if the rate of change of the real part of the impedance is less than 2 times the standard deviation corresponding to the real axis, or the rate of change of the imaginary part of the impedance is less than 2 times the standard deviation corresponding to the real axis. This prevents spectral leakage and harmonic pollution.

[0089] Step S1047: The result of multiplying the first weight, second weight, third weight and fourth weight corresponding to each sampling frequency is determined as the impedance weight of the solid battery at each sampling frequency.

[0090] Step S105: Based on the impedance weight of the solid-state battery at each sampling frequency, the impedance of the solid-state battery at each sampling frequency, and the theoretical impedance of the solid-state battery at each sampling frequency, determine the impedance-related parameters of the solid-state battery.

[0091] Here, the impedance-related parameters are calibrated using a weighted complex domain least squares method. The impedance-related parameters corresponding to different frequency bands to be calibrated are... ,in, For impedance, For impedance weighting, This is the theoretical impedance. Step S106: Compare the impedance-related parameters with the theoretical impedance-related parameters corresponding to the current state.

[0092] Step S107: When the comparison result between the impedance-related parameters and the theoretical impedance-related parameters corresponding to the current state meets the first preset fault condition, it is determined that the solid-state battery has malfunctioned.

[0093] Here, the comparison results between the impedance-related parameters and the theoretical impedance-related parameters corresponding to the current state are combined with empirical thresholds or trend judgment rules to identify abnormal states. These abnormal states are then updated online via the BMS for timely response and processing, enabling real-time diagnosis of the health status and safety risks of solid-state batteries. Specifically, 1. In R... int Compared to theoretical R int Rising by more than 20%, Q int Compared to theoretical Q int A decline of more than 15%, and alpha int Compared to theoretical α int If at least two conditions are met, a reduction exceeding 0.05 indicates severe contact failure at the electrode / electrolyte interface. Countermeasures include: limiting charging power to 50%, reducing discharging power to 80%, adjusting the SOC window to 20-80% to avoid interface polarization deterioration under high SOC, and setting the thermal management target temperature to 25°C. While parked, initiate a short-range reactivation strategy, performing 1-2 charge-discharge cycles with a small current of 0.2-0.3C, each with a ΔSOC of approximately 5-10%, and observe R... ct Has there been any improvement? If the battery structure is adjustable, it is recommended to increase the clamping force to 70% of the calibrated upper limit. Re-perform the EIS test within 72 hours, including the low-frequency band. If R int If the trend continues to worsen or shows no improvement, the module should be shut down and inspected or replaced with a solid-state battery. 2. In R gb Compared to theoretical R gb Rising by more than 20%, Q gb Compared to theoretical Q gb A decline of more than 15%, and alpha gb Compared to theoretical αgb If at least two conditions are met for a reduction exceeding 0.05, it can be determined that there is severe grain boundary degradation or phase transition on the solid electrolyte side. Countermeasures include: limiting charging power to 50%, reducing discharging power to 80%, adjusting the SOC window to 20-80%, and setting the thermal management target temperature to 25°C. In parked conditions, implement a temperature zone avoidance + slow charging strategy, performing slow charging at 0.2-0.3C to 70-80% SOC at 25-30°C, followed by a static retest. If no improvement is observed after two consecutive parked retests, it is considered severe degradation, and the service life should be further shortened and replacement planned. 3. When σ increases by more than 20% compared to the theoretical σ, it can be determined that there is severe diffusion restriction or dendrite growth risk. Countermeasures include: limiting charging power to 50%, reducing discharging power to 80%, adjusting the SOC window to 20-80%, and setting the thermal management target temperature to 25°C. Avoid ambient temperatures dropping below 10°C; if the temperature drops, further reduce the power by 20%. Implement a "conservative SOC" strategy, maintaining the daily SOC between 30-70%. If the device is equipped with an adjustable pressure system, increase the pressure to 80-90% of the rated upper limit and perform a slow charging cycle retest at 25-30°C. If increased self-discharge, abnormal noise, or abnormal cell temperature is detected, it is considered that dendrites have entered a serious state. Immediately disable fast charging mode, activate limp-home power, and arrange for maintenance as soon as possible.

[0094] In one possible implementation, the monitoring method further includes: determining the temperature-sensitive slope of the solid-state battery based on the bulk impedance, the grain boundary impedance, and the absolute temperature of the solid-state battery; and determining that the solid-state battery has malfunctioned when the temperature-sensitive slope meets a second preset fault condition.

[0095] Specifically, compare whether the "temperature-sensitive slope" deviates from the normal range over a recent period (at least 3 different temperature points). Two slopes are defined here: the first slope S... b (The calculation formula is S) b =slope(ln(1 / R b ),1 / T))and the second slope S gb (The calculation formula is S) gb =slope(ln(1 / R gb ),1 / T). If the relative deviation between the two is |(S gb -S b )| / |S bAn increase exceeding 25% indicates thermal mismatch, potentially suggesting a phase transition, changes in component ratios, or the emergence of a new impedance mechanism. To address this, the following measures should be taken: Define a specific risk temperature zone, prohibiting fast charging within this range and reducing power by 20%, while strengthening cold / heat management measures to shorten the battery's residence time in this zone. Re-test full-frequency electrochemical impedance spectroscopy (EIS) in a temperature range outside the risk zone (e.g., 25-30°C). Update the temperature-dependent parameter table with the newly measured slope data and update the power estimation model accordingly to accommodate this change. Furthermore, if the risk temperature zone highly overlaps with local climatic conditions, charging limitations and maintenance reminders should be sent to users via the vehicle's online update system (OTA) or onboard information system to ensure users are aware of the potential risks and take appropriate precautions.

[0096] In this application embodiment, by integrating multi-frequency electrochemical impedance spectroscopy (EIS) excitation, PID inflection point identification technology and AC ripple extraction method, it is possible to monitor the interface impedance change and contact failure of solid-state batteries online, non-destructively and in real time without disassembling the battery or interfering with its normal operation, thereby improving the safety and service life of the battery. (1) This application abandons the expensive and bulky electrochemical workstation and aims to effectively embed advanced EIS monitoring functions into the existing battery management system (BMS) with the lowest additional hardware cost and the highest system integration. (2) This application overcomes the disadvantages of traditional EIS technology, such as slow measurement speed and inability to be applied online, and solves the limitation that the AC ripple method can only provide limited frequency point information. It can quickly acquire broadband impedance spectrum information in seconds, effectively solving the contradiction between "real-time" and "full-band information". (3) This application is committed to how to effectively integrate broadband EIS data, AC ripple data and traditional BMS data, and use these fused information to perform accurate and robust real-time diagnosis and prediction of the health status of solid-state batteries.

[0097] This application provides a monitoring method for solid-state batteries. This method enables real-time diagnosis of solid-state batteries, provides early and accurate warnings of interface failures, improves battery safety, reduces system cost, complexity, and integration difficulty, and enhances the anti-interference capability and reliability of monitoring under complex operating conditions.

[0098] Based on the same application concept, this application also provides a solid-state battery monitoring device corresponding to the solid-state battery monitoring method provided in the above embodiments. Since the principle of the device in this application to solve the problem is similar to the solid-state battery monitoring method in the above embodiments of this application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0099] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a monitoring device for a solid-state battery provided in an embodiment of this application.

[0100] like Figure 3 As shown in the illustration, the solid-state battery monitoring device 310 provided in this application embodiment includes: The acquisition module 311 is used to acquire the first impedance spectrum data of the solid-state battery; wherein, the first impedance spectrum data includes the impedance of the solid-state battery at multiple sampling frequencies; Extraction module 312 is used to extract the theoretical impedance-related parameters of the solid-state battery in the current state from the mapping table used to characterize the mapping relationship between the solid-state battery state and the theoretical impedance-related parameters of the solid-state battery. Theoretical impedance determination module 313 is used to determine the theoretical impedance of the solid-state battery at each sampling frequency based on the theoretical impedance-related parameters corresponding to the current state. The weight determination module 314 is used to determine the impedance weight of the solid-state battery at each sampling frequency based on the first impedance spectrum data. The parameter determination module 315 is used to determine the impedance-related parameters of the solid-state battery based on the impedance weight of the solid-state battery at each sampling frequency, the impedance of the solid-state battery at each sampling frequency, and the theoretical impedance of the solid-state battery at each sampling frequency. Comparison module 316 is used to compare the impedance-related parameters with the theoretical impedance-related parameters corresponding to the current state; The first anomaly judgment module 317 is used to determine that the solid-state battery has an anomaly when the comparison result between the impedance-related parameters and the theoretical impedance-related parameters corresponding to the current state meets the first preset fault condition.

[0101] Furthermore, the monitoring device 310 includes a construction module; the construction module 318 is specifically used for: For each test state of the solid-state battery in multiple test states, when the solid-state battery is in that test state, the second impedance spectrum data of the solid-state battery is acquired; Based on the second impedance spectrum data, the theoretical impedance-related parameters of the solid-state battery in this test state are determined. The mapping table is constructed based on the theoretical impedance parameters of the solid-state battery under all test conditions.

[0102] Furthermore, the second impedance spectrum data includes: a Nyquist curve characterizing the relationship between the real and imaginary parts of the solid-state battery impedance, and the phase of the solid-state battery at multiple test frequencies; the construction module 318, when determining the theoretical impedance-related parameters of the solid-state battery corresponding to this test state based on the second impedance spectrum data, is also specifically used for: For each test state, determine the preset impedance frequency band and preset diffusion impedance frequency band corresponding to that test state; Based on the Nyquist curve, the initial impedance-related parameters corresponding to each impedance band of the preset impedance frequency band are determined; for each impedance band, the initial impedance-related parameters corresponding to the impedance band are iterated based on the impedance corresponding to the impedance band. Based on the type of Nyquist curve corresponding to the preset diffusion impedance frequency band, the diffusion impedance of the solid-state battery is determined; based on the diffusion impedance, the initial impedance-related parameters corresponding to the preset diffusion impedance frequency band are iterated. The initial impedance-related parameters corresponding to the preset impedance frequency band and the initial impedance-related parameters corresponding to the preset diffusion impedance frequency band are determined as the theoretical impedance-related parameters of the solid-state battery.

[0103] Furthermore, when the construction module 318 is used to determine the initial impedance-related parameters corresponding to each impedance frequency band of the preset impedance frequency band based on the Nyquist curve, it is also specifically used for: For each impedance frequency band of the preset impedance frequency band, if the Nyquist curve corresponding to the impedance frequency band has a peak, then the test frequency corresponding to the peak is determined as the center frequency corresponding to the impedance frequency band. If the Nyquist curve corresponding to the impedance band does not have a peak, then the lower limit frequency of the impedance band is determined as the center frequency corresponding to the impedance band. Based on the center frequency corresponding to the impedance band, determine the initial impedance-related parameters corresponding to the impedance band.

[0104] Furthermore, the first impedance spectrum data also includes: the phase of the solid-state battery at multiple sampling frequencies and the amplitude of the solid-state battery at multiple sampling frequencies; the weight determination module 314 is specifically used for: For each sampling frequency, the phase standard deviation corresponding to that sampling frequency is determined based on the phase corresponding to that sampling frequency; and the first weight corresponding to that sampling frequency is determined based on the phase standard deviation. Based on the amplitude corresponding to the sampling frequency, determine the standard deviation of the impedance amplitude corresponding to the sampling frequency and the mean of the impedance amplitude corresponding to the sampling frequency; based on the standard deviation of the impedance amplitude and the mean, determine the second weight corresponding to the sampling frequency; Based on the energy corresponding to the sampling frequency and the total energy of the sidelobe harmonics corresponding to the sampling frequency, the third weight corresponding to the sampling frequency is determined. Based on the rate of change of the real part and the rate of change of the imaginary part of the impedance corresponding to the sampling frequency, the fourth weight corresponding to the sampling frequency is determined. The result of multiplying the first, second, third, and fourth weights corresponding to each sampling frequency is determined as the impedance weight of the solid-state battery at each sampling frequency.

[0105] Furthermore, the monitoring device 310 also includes: The preset frequency band determination module 319 is used to determine the preset frequency band corresponding to the vehicle's operating status based on the vehicle's operating status. The injection module 320 is used to inject an excitation current corresponding to a preset frequency band into the solid-state battery in order to obtain the first impedance spectrum data of the solid-state battery.

[0106] Furthermore, the impedance-related parameters include: bulk impedance and grain boundary impedance; the monitoring device 310 also includes: The temperature-sensitive slope determination module 321 is used to determine the temperature-sensitive slope of the solid-state battery based on the bulk impedance, the grain boundary impedance and the absolute temperature of the solid-state battery. The second anomaly determination module 322 is used to determine that the solid-state battery has an anomaly when the temperature-sensitive slope meets the second preset fault condition.

[0107] This application provides a solid-state battery monitoring device that enables real-time diagnosis of solid-state batteries, provides early and accurate warnings of interface failures, improves battery safety, reduces system cost, complexity, and integration difficulty, and enhances the anti-interference capability and reliability of monitoring under complex operating conditions.

[0108] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0109] like Figure 4 As shown, the electronic device 400 includes a processor 410, a memory 420, and a bus 430.

[0110] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 communicates with the memory 420 via the bus 430. When the machine-readable instructions are executed by the processor 410, they can perform the operations described above. Figure 1 The steps of the solid-state battery monitoring method in the illustrated method embodiment can be found in the method embodiment for specific implementation, and will not be repeated here.

[0111] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1The steps of the solid-state battery monitoring method in the illustrated method embodiment can be found in the method embodiment for specific implementation, and will not be repeated here.

[0112] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0113] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0115] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0116] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of monitoring a solid-state battery, characterized by, The monitoring method comprises: obtaining first impedance spectrum data of the solid-state battery; wherein the first impedance spectrum data comprises impedances of the solid-state battery corresponding to a plurality of sampling frequencies; extracting, from a mapping table for mapping a relationship between a theoretical impedance-related parameter of the solid-state battery and a state of the solid-state battery, a theoretical impedance-related parameter corresponding to a current state of the solid-state battery; determining, based on the theoretical impedance-related parameter corresponding to the current state, a theoretical impedance of the solid-state battery corresponding to each sampling frequency; determining, based on the first impedance spectrum data, an impedance weight of the solid-state battery corresponding to each sampling frequency; determining, based on the impedance weight of the solid-state battery corresponding to each sampling frequency, the impedance of the solid-state battery corresponding to each sampling frequency, and the theoretical impedance of the solid-state battery corresponding to each sampling frequency, an impedance-related parameter of the solid-state battery; comparing the impedance-related parameter with the theoretical impedance-related parameter corresponding to the current state; when a comparison result of the impedance-related parameter and the theoretical impedance-related parameter corresponding to the current state meets a first preset fault condition, determining that the solid-state battery is abnormal.

2. The method of monitoring a solid state battery of claim 1, wherein, The mapping table is constructed in the following manner: for each test state of the solid-state battery in a plurality of test states, obtaining second impedance spectrum data of the solid-state battery when the solid-state battery is in the test state; determining, based on the second impedance spectrum data, a theoretical impedance-related parameter corresponding to the test state of the solid-state battery; based on the theoretical impedance-related parameters corresponding to all test states of the solid-state battery, constructing the mapping table.

3. The method of monitoring a solid state battery of claim 2, wherein, The second impedance spectrum data comprises a Nyquist curve for representing a relationship between a real part of the impedance of the solid-state battery and an imaginary part of the impedance of the solid-state battery, and phases of the solid-state battery corresponding to a plurality of test frequencies; and the determining, based on the second impedance spectrum data, of the theoretical impedance-related parameter corresponding to the test state of the solid-state battery comprises: for each test state, determining a preset impedance frequency band and a preset diffusion impedance frequency band corresponding to the test state; based on the Nyquist curve, determining an initial impedance-related parameter corresponding to each impedance frequency band of the preset impedance frequency band; and for each impedance frequency band, based on an impedance corresponding to the impedance frequency band, iteratively determining the initial impedance-related parameter corresponding to the impedance frequency band; based on a type of the Nyquist curve corresponding to the preset diffusion impedance frequency band, determining a diffusion impedance of the solid-state battery; and based on the diffusion impedance, iteratively determining the initial impedance-related parameter corresponding to the preset diffusion impedance frequency band; determining the initial impedance-related parameter corresponding to the preset impedance frequency band after iteration and the initial impedance-related parameter corresponding to the preset diffusion impedance frequency band as the theoretical impedance-related parameter of the solid-state battery.

4. The method of monitoring a solid state battery of claim 3, wherein, The determining, based on the Nyquist curve, of the initial impedance-related parameter corresponding to each impedance frequency band of the preset impedance frequency band comprises: for each impedance frequency band of the preset impedance frequency band, if the Nyquist curve corresponding to the impedance frequency band has a peak value, determining a test frequency corresponding to the peak value as a center frequency corresponding to the impedance frequency band; if the Nyquist curve corresponding to the impedance frequency band does not have a peak value, determining a lower limit frequency of the impedance frequency band as the center frequency corresponding to the impedance frequency band. Determine an initial impedance-related parameter corresponding to the impedance frequency band based on a center frequency corresponding to the impedance frequency band.

5. The method of monitoring a solid state battery of claim 1, wherein, The first impedance spectrum data further includes phases of the solid-state battery corresponding to the plurality of sampling frequencies and amplitudes of the solid-state battery corresponding to the plurality of sampling frequencies; and determining the impedance weight of the solid-state battery corresponding to each sampling frequency based on the first impedance spectrum data includes: For each sampling frequency, determining a phase standard deviation corresponding to the sampling frequency based on the phase corresponding to the sampling frequency; and determining a first weight corresponding to the sampling frequency based on the phase standard deviation; Determining an impedance amplitude standard deviation corresponding to the sampling frequency and a mean value of the impedance amplitude corresponding to the sampling frequency based on the amplitude corresponding to the sampling frequency; and determining a second weight corresponding to the sampling frequency based on the impedance amplitude standard deviation and the mean value; Determining a third weight corresponding to the sampling frequency based on the energy corresponding to the sampling frequency and a total energy of the side lobe harmonics corresponding to the sampling frequency; Determining a fourth weight corresponding to the sampling frequency based on a change rate of the impedance real part corresponding to the sampling frequency and a change rate of the impedance imaginary part; Multiplying the first weight, the second weight, the third weight, and the fourth weight corresponding to each sampling frequency to obtain the impedance weight of the solid-state battery corresponding to each sampling frequency.

6. The method of monitoring a solid state battery of claim 1, wherein, Before obtaining the first impedance spectrum data of the solid-state battery, the monitoring method further includes: Determining a preset frequency band corresponding to the operating state of the vehicle according to the operating state of the vehicle; Injecting an excitation current corresponding to the preset frequency band into the solid-state battery to obtain the first impedance spectrum data of the solid-state battery.

7. The method of monitoring a solid state battery of claim 1, wherein, The impedance-related parameter includes a bulk impedance and a grain boundary impedance. After determining that the comparison result of the impedance-related parameter and the theoretical impedance-related parameter corresponding to the current state satisfies a first preset fault condition, the monitoring method further includes: Determining a temperature-sensitive slope of the solid-state battery based on the bulk impedance, the grain boundary impedance, and an absolute temperature of the solid-state battery; When the temperature-sensitive slope satisfies a second preset fault condition, determining that the solid-state battery is abnormal.

8. A monitoring device for a solid state battery, characterized by The monitoring device includes: An obtaining module configured to obtain first impedance spectrum data of a solid-state battery, wherein the first impedance spectrum data includes impedances of the solid-state battery corresponding to a plurality of sampling frequencies; An extracting module configured to extract a theoretical impedance-related parameter corresponding to a current state of the solid-state battery from a mapping table used to represent a mapping relationship between the state of the solid-state battery and the theoretical impedance-related parameter of the solid-state battery; A theoretical impedance determining module configured to determine a theoretical impedance of the solid-state battery corresponding to each sampling frequency based on the theoretical impedance-related parameter corresponding to the current state; A weight determining module configured to determine an impedance weight of the solid-state battery corresponding to each sampling frequency based on the first impedance spectrum data; A parameter determining module configured to determine an impedance-related parameter of the solid-state battery based on the impedance weight of the solid-state battery corresponding to each sampling frequency, the impedance of the solid-state battery corresponding to each sampling frequency, and the theoretical impedance of the solid-state battery corresponding to each sampling frequency; A comparison module configured to compare the impedance-related parameter with the theoretical impedance-related parameter corresponding to the current state. The first abnormality judging module is configured to determine that the solid-state battery has an abnormality when a comparison result of the impedance-related parameter and a theoretical impedance-related parameter corresponding to the current state satisfies a first preset fault condition.

9. An electronic device, comprising: The application further provides a computer readable storage medium having stored thereon a computer program, wherein the computer program is executed by a processor to perform the steps of the monitoring method of the solid-state battery according to any one of claims 1 to 7. The application further provides a computer readable storage medium having stored thereon a computer program, wherein the computer program is executed by a processor to perform the steps of the monitoring method of the solid-state battery according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, ​

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