A method, device, equipment and storage medium for detecting SoH of energy storage battery

Abstract

The present invention discloses a method, device, equipment and storage medium for detecting SoH of an energy storage battery. The method comprises: determining an impedance spectrum under the current ambient temperature and the current battery power SOC; constructing a full-frequency equivalent circuit model structure according to the impedance spectrum; extracting a medium- and low-frequency equivalent circuit model structure in the full-frequency equivalent circuit model structure; determining medium- and low-frequency equivalent circuit model parameters based on the medium- and low-frequency equivalent circuit model structure, the impedance spectrum and a preset parameter identification model; inputting the medium- and low-frequency equivalent circuit model parameters, the current temperature parameters and the current battery power SOC parameters into a neural network model to determine the SoH of the energy storage battery. This solution achieves accurate prediction of the SoH of the energy storage battery through the impedance spectrum.

Description

Technical Field

[0001] The embodiments of the present invention relate to energy storage battery technology, and in particular to a method, device, equipment and storage medium for detecting SoH of an energy storage battery. Background Art

[0002] Energy storage can effectively deal with the strong volatility and intermittency of wind, solar and other renewable energy power generation. It is also an important means to greatly enhance the grid's ability to absorb renewable energy power generation. It is one of the important components of smart grids and microgrids at all levels. As an energy storage element, lithium batteries have the advantages of high energy and power density, long life, and no memory effect. They have been widely used in the field of energy storage. However, with the increase in the number of lithium battery charge and discharge cycles, the internal resistance of lithium batteries increases, the active substances decrease, and the state of health (SoH, SoH) continues to decay, increasing the risk of thermal runaway in large-scale energy storage battery packs, which is not conducive to the safe and stable operation of energy storage power stations. As an indicator for quantitative analysis of battery safety, accurate estimation of SoH can ensure the safe operation of energy storage batteries and provide a basis for formulating the next operation and maintenance plan.

[0003] The existing method for estimating the SoH of an energy storage battery is to directly collect the current and voltage external characteristic data of the energy storage battery, and obtain an equivalent circuit model through the current and voltage data, and then estimate the SoH of the energy storage battery. Since in the prior art, collecting the current and voltage data of the energy storage battery is a static process, and the current and voltage data of the energy storage battery are external characteristic data, the method of using the equivalent circuit model to predict the SoH of the energy storage battery has poor generalization ability and is not suitable for predicting the SoH of the energy storage battery under dynamic conditions, and its prediction result is inaccurate. Summary of the invention

[0004] The present invention provides a method, device, equipment and storage medium for detecting SoH of an energy storage battery, so as to accurately predict the SoH of the energy storage battery through an impedance spectrum.

[0005] In a first aspect, an embodiment of the present invention provides a method for detecting SoH of an energy storage battery, the method comprising:

[0006] Determine the impedance spectrum under the current ambient temperature and the current battery charge SOC;

[0007] Constructing a full-frequency equivalent circuit model structure according to the impedance spectrum;

[0008] Extracting a medium- and low-frequency equivalent circuit model structure from the full-frequency equivalent circuit model structure;

[0009] Determine medium and low frequency equivalent circuit model parameters based on the medium and low frequency equivalent circuit model structure, the impedance spectrum and a preset parameter identification model;

[0010] The medium and low frequency equivalent circuit model parameters, the current ambient temperature parameters and the current battery power SOC parameters are input into the neural network model to determine the energy storage battery SoH.

[0011] Optionally, determine the impedance spectrum under the current ambient temperature and the current battery power SOC, including:

[0012] An impedance spectrum excitation circuit is set to output excitation signals of different frequencies; wherein the impedance spectrum excitation circuit includes a preset number of shift registers and XOR gates;

[0013] Under the excitation signals of different frequencies, the current value and voltage value of the energy storage battery to be tested under the current ambient temperature and the current battery power SOC are obtained;

[0014] The impedance spectrum under the current ambient temperature and the current battery power SOC is determined according to the excitation signals of different frequencies, the current ambient temperature and the current battery power SOC of the energy storage battery to be tested.

[0015] Optionally, the parameter identification model is a nonlinear least squares regression model;

[0016] The energy storage battery SoH detection method also includes:

[0017] Preprocessing the impedance spectrum to obtain a complex domain impedance spectrum;

[0018] A full-frequency equivalent circuit model structure is constructed according to the impedance spectrum, including:

[0019] Constructing a full-frequency equivalent circuit model structure according to the complex domain impedance spectrum;

[0020] Determining medium and low frequency equivalent circuit model parameters based on the medium and low frequency equivalent circuit model structure and the impedance spectrum in combination with a preset parameter identification model includes:

[0021] The medium and low frequency equivalent circuit model parameters are determined based on the medium and low frequency equivalent circuit model structure, the complex domain impedance spectrum and the nonlinear least squares regression model.

[0022] Optionally, also include:

[0023] Extracting medium and low frequency equivalent circuit model associated parameters from the medium and low frequency equivalent circuit model parameters;

[0024] Inputting the medium and low frequency equivalent circuit model parameters, the current ambient temperature parameters and the current battery power SOC parameters into the neural network model to determine the energy storage battery SoH, including:

[0025] The medium and low frequency equivalent circuit model association parameters, current ambient temperature parameters and current battery power SOC parameters are input into the neural network model to determine the energy storage battery SoH.

[0026] Optionally, the medium and low frequency equivalent circuit model structure includes: a first resistor, a second resistor, a first solid-state diffusion impedance and a second solid-state diffusion impedance;

[0027] The second resistor is electrically connected in parallel with the first solid-state diffusion impedance, the first resistor and the second resistor are electrically connected in series, and the second solid-state diffusion impedance is electrically connected in series with the first solid-state diffusion impedance;

[0028] Determining the parameters of the medium and low frequency equivalent circuit model based on the medium and low frequency equivalent circuit model structure, the complex domain impedance spectrum and a preset parameter identification model includes:

[0029] Based on the medium and low frequency equivalent circuit model structure, the complex domain impedance spectrum and a preset parameter identification model, a first resistance parameter of the first resistor, a second resistance parameter of the second resistor, a first capacitance parameter and a first proportional parameter of the first solid-state diffusion impedance, and a second capacitance parameter and a second proportional parameter of the second solid-state diffusion impedance are determined.

[0030] Optionally, extracting medium and low frequency equivalent circuit model associated parameters from the medium and low frequency equivalent circuit model parameters includes:

[0031] Extract the first resistance parameter of the first resistor, the second resistance parameter of the second resistor, the first capacitance parameter and the first proportional parameter of the first solid-state diffusion impedance, the second capacitance parameter and the second proportional parameter of the second solid-state diffusion impedance.

[0032] Optionally, the neural network model includes a gated recurrent unit neural network;

[0033] The energy storage battery SoH detection method further includes:

[0034] The gated recurrent unit neural network model is determined according to historical low-frequency equivalent circuit model parameters, different ambient temperature parameters and different battery power SOC parameters.

[0035] In a second aspect, an embodiment of the present invention further provides a SoH detection device for an energy storage battery, the device comprising:

[0036] An impedance spectrum determination module is used to determine the impedance spectrum under the current ambient temperature and the current battery power SOC;

[0037] A circuit model building module, used to build a full-frequency equivalent circuit model structure according to the impedance spectrum;

[0038] An extraction module, used to extract the medium and low frequency equivalent circuit model structure in the full frequency equivalent circuit model structure;

[0039] A circuit model parameter determination module, used to determine the medium and low frequency equivalent circuit model parameters based on the medium and low frequency equivalent circuit model structure, the impedance spectrum and a preset parameter identification model;

[0040] The energy storage battery SoH determination module is used to input the medium and low frequency equivalent circuit model parameters, the current ambient temperature parameters and the current battery power SOC parameters into the neural network model to determine the energy storage battery SoH.

[0041] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0042] at least one processor; and

[0043] a memory communicatively connected to the at least one processor; wherein,

[0044] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the energy storage battery SoH detection method described in the first aspect.

[0045] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the energy storage battery SoH detection method described in the first aspect when executed.

[0046] In an embodiment of the present invention, an impedance spectrum under the current ambient temperature and the current battery power SOC is determined; a full-frequency equivalent circuit model structure is constructed according to the impedance spectrum; a medium- and low-frequency equivalent circuit model structure in the full-frequency equivalent circuit model structure is extracted; medium- and low-frequency equivalent circuit model parameters are determined based on the medium- and low-frequency equivalent circuit model structure and the impedance spectrum in combination with a preset parameter identification model; and the medium- and low-frequency equivalent circuit model parameters, current temperature parameters, and current battery power SOC parameters are input into a neural network model to determine the SoH of an energy storage battery, thereby achieving accurate prediction of the SoH of an energy storage battery through the impedance spectrum. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of a method for detecting SoH of an energy storage battery provided by an embodiment of the present invention;

[0048] Figure 2is a flow chart of another energy storage battery SoH detection method provided by an embodiment of the present invention;

[0049] Figure 3 is a schematic diagram of the structure of an impedance spectrum excitation circuit provided by an embodiment of the present invention;

[0050] Figure 4 is a structural schematic diagram of a full-frequency equivalent circuit model structure provided by an embodiment of the present invention;

[0051] Figure 5 is a structural schematic diagram of a medium and low frequency equivalent circuit model structure provided by an embodiment of the present invention;

[0052] Figure 6 It is a structural schematic diagram of a SoH detection device for an energy storage battery provided by an embodiment of the present invention;

[0053] Figure 7 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0055] Figure 1 is a flow chart of a method for detecting SoH of an energy storage battery provided by an embodiment of the present invention. This embodiment can be applied to detecting the SoH condition of an energy storage battery. The method can be executed by an energy storage battery SoH detection device, such as Figure 1 As shown, the method specifically comprises the following steps:

[0056] S110 , determining an impedance spectrum under the current ambient temperature and the current battery power SOC.

[0057] Among them, the impedance spectrum can be measured by the electrochemical impedance method. Generally, the electrochemical impedance method is a process of applying a small-amplitude AC sinusoidal potential wave with different frequencies to the energy storage battery system to be tested, thereby measuring the ratio of the AC potential to the current signal (the impedance of the energy storage battery system to be tested) as the sinusoidal wave frequency w changes to obtain the impedance spectrum. In the actual cycle aging test of the energy storage battery, the impedance spectrum uniquely determined by the current ambient temperature and the current battery charge SOC can be collected.

[0058] S120. Construct a full-frequency equivalent circuit model structure based on the impedance spectrum.

[0059] Among them, the full-frequency equivalent circuit model structure includes a low-frequency band equivalent circuit model structure, a mid-frequency band equivalent circuit model structure and a high-frequency end equivalent circuit model; the equivalent impedance change of the equivalent circuit model structure of each frequency band is consistent with the impedance change of the impedance spectrum of the corresponding frequency band; the impedance spectrum can construct a full-frequency band equivalent circuit model structure based on the principle of consistency of equivalent impedance change.

[0060] S130, extracting the medium and low frequency equivalent circuit model structure from the full frequency equivalent circuit model structure.

[0061] Among them, since the equivalent impedances in the medium and low frequency bands of the impedance spectrum are sensitive features that affect the health of lithium batteries, and the equivalent impedances in the medium and high frequency bands of the impedance spectrum are less sensitive features that affect the health of lithium batteries; correspondingly, extracting the medium and low frequency equivalent circuit model structure in the full-frequency equivalent circuit model structure can save data calculation time for the subsequent prediction of lithium battery health SoH.

[0062] S140, determining the structural parameters of the medium and low frequency equivalent circuit model based on the medium and low frequency equivalent circuit model structure, impedance spectrum and a preset parameter identification model.

[0063] Among them, the preset parameter identification model is a pre-trained parameter identification model, which can be used to realize the identification of parameters in the medium and low frequency equivalent circuit model structure; specifically, in the actual cycle aging test of the energy storage battery, the impedance spectra of different ambient temperatures and different battery power SOCs can be collected at intervals of 10% battery power SOC and 5°C ambient temperature to obtain a dynamically changing impedance spectrum; different full-frequency equivalent circuit model structures can be constructed according to the dynamically changing impedance spectrum, and different medium and low frequency equivalent circuit model structures can be extracted from different full-frequency equivalent circuit model structures; and the preset parameter identification model is obtained by training different medium and low frequency equivalent circuit model structures and dynamically changing impedance spectra; then the current impedance spectrum and the medium and low frequency equivalent circuit model structure determined by the current impedance spectrum are input into the trained preset parameter identification model, and the medium and low frequency equivalent circuit model structure parameters can be obtained.

[0064] S150, inputting the structural parameters of the medium and low frequency equivalent circuit model, the current temperature parameters and the current battery power SOC parameters into the neural network model to determine the energy storage battery SoH.

[0065] Among them, the neural network model is a pre-trained network model; specifically, the neural network model can be trained according to the historical medium and low frequency equivalent circuit model parameters, different ambient temperature parameters and different battery power SOC parameters. It should be noted here that the historical medium and low frequency equivalent circuit model parameters are different historical medium and low frequency equivalent circuit model structures, impedance spectra under different ambient temperatures and different battery power SOC in the energy storage battery charge and discharge cycle loop, which are input into the trained preset parameter identification model to obtain different historical medium and low frequency equivalent circuit model structure parameters; the current temperature parameters, current battery power SOC parameters and current medium and low frequency equivalent circuit model structure parameters are input into the trained neural network model to determine the current energy storage battery SoH.

[0066] The present invention uses the external characteristic data of the impedance spectrum and constructs a full-frequency equivalent circuit model structure based on the external characteristic data of the impedance spectrum, extracts the medium- and low-frequency equivalent circuit model structure in the full-frequency equivalent circuit model structure; determines the medium- and low-frequency equivalent circuit model parameters based on the medium- and low-frequency equivalent circuit model structure and the impedance spectrum in combination with a preset parameter identification model; inputs the medium- and low-frequency equivalent circuit model parameters, current ambient temperature parameters and current battery power SOC parameters into a neural network model to determine the SoH of an energy storage battery, thereby achieving accurate prediction of the SoH of an energy storage battery with a relatively fast prediction speed.

[0067] Optionally, based on the above embodiment, further optimization is performed: Figure 2 FIG. 1 is a flow chart of another method for detecting SoH of an energy storage battery provided by an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0068] S210 , determining an impedance spectrum under the current ambient temperature and the current battery power SOC.

[0069] In this embodiment, the impedance spectrum can be determined based on the impedance spectrum excitation circuit; specifically, the impedance spectrum excitation circuit is set to output excitation signals of different frequencies; Figure 3 is a schematic diagram of the structure of the impedance spectrum excitation circuit provided by an embodiment of the present invention, such as Figure 3As shown, the impedance spectrum excitation circuit includes a preset number of shift registers K and an XOR gate G; when a fixed clock signal is input to the impedance spectrum excitation circuit, the number of shift registers K is changed so that the circuit outputs excitation signals of different frequencies; under the excitation signals of different frequencies, the current value and voltage value of the energy storage battery to be tested under the current ambient temperature and the current battery power SOC are obtained; according to the excitation signals of different frequencies, the current value and voltage value of the energy storage battery to be tested under the current ambient temperature and the current battery power SOC, the impedance spectrum under the current ambient temperature and the current battery power SOC is determined. Due to the simple structure of the impedance spectrum excitation circuit, the speed of determining the impedance spectrum based on the impedance spectrum excitation circuit is fast, and the circuit can be widely used in different application scenarios.

[0070] S220, preprocessing the impedance spectrum to obtain a complex domain impedance spectrum.

[0071] Specifically, the current values ​​and voltage values ​​at each frequency w on the impedance spectrum obtained under the current ambient temperature and the current battery power SOC are subjected to Fourier transformation to obtain V(jw) and I(jw) respectively, and the corresponding battery impedance Z(jw) after Fourier transformation is obtained as follows:

[0072]

[0073] And determine the amplitude |Z(jw)| and phase θ of the battery impedance Z(jw) based on V(jw) and I(jw) z (jw) are:

[0074]

[0075] θ z (jw) = θ V (jw)-θ i (jw)

[0076] Therefore, the battery impedance Z(jw) can be specifically defined as: Z(jw)=Z(jw) Re +jZ(jw) Im ;

[0077] Z(jw) Re =|Z(jw)|cosθ z (jw)

[0078] Z(jw) Im = |Z(jw)|sinθ z (jw)

[0079] The real part of the battery impedance Z(jw) Re As the abscissa, the imaginary part of the battery impedance Z(jw) Im As the ordinate, the complex domain impedance spectrum can be obtained.

[0080] S230. Construct a full-frequency equivalent circuit model structure based on the complex domain impedance spectrum.

[0081] in, Figure 4 is a schematic diagram of the structure of the full-frequency equivalent circuit model provided by an embodiment of the present invention, such as Figure 4 As shown, according to the consistency between the equivalent resistance change on the complex domain impedance spectrum l at each frequency and the equivalent impedance change of the full-frequency equivalent circuit model structure, the full-frequency equivalent circuit model structure can be equivalent to a high-frequency band equivalent circuit model structure 001, a mid-frequency band equivalent circuit model structure 002 and a low-frequency band equivalent circuit model structure 002; the high-frequency band equivalent circuit model structure 001 is composed of a charge transfer impedance in the high-frequency domain formed by the inductor L, the resistor r1 and the resistor r2 in parallel with the solid diffused capacitor CPE3; the mid-frequency band equivalent circuit model structure 002 includes: a first resistor R0, a second resistor R1, and a first solid diffused impedance CPE1; the low-frequency band equivalent circuit model structure 003 includes a second solid diffused impedance CPE2; the second resistor R1 is electrically connected to the first solid diffused impedance CPE1 in parallel, the first resistor R0 and the second resistor R1 are electrically connected in series, and the second solid diffused impedance CPE2 is electrically connected to the first solid diffused impedance CPE1 in series;

[0082] The impedance expression of the first solid-state diffusion impedance CPE1 is:

[0083]

[0084] The above Q CPE1 (jw) is the equivalent capacitance value of the first solid diffusion impedance; α CPE1 is the diffusion index of the first solid-state diffusion impedance; α CPE1 Adjustable changes;

[0085] The impedance expression of the second solid diffusion impedance CPE2 is:

[0086]

[0087] The above Q CPE2 (jw) is the equivalent capacitance value of the second solid diffusion impedance; α CPE2 is the diffusion index of the second solid-state diffusion impedance; α CPE2 The expression of solid diffusion capacitance CPE3 is similar.

[0088] S240, extracting the medium and low frequency equivalent circuit model structure from the full frequency equivalent circuit model structure.

[0089] in, Figure 5 is a structural schematic diagram of a medium and low frequency equivalent circuit model structure provided by an embodiment of the present invention, such as Figure 5As shown, the extracted medium and low frequency equivalent circuit model structure is a first resistor R0, a second resistor R1, a first solid-state diffusion impedance CPE1 and a second solid-state diffusion impedance CPE2.

[0090] S250, determine the parameters of the medium and low frequency equivalent circuit model based on the medium and low frequency equivalent circuit model structure, complex domain impedance spectrum and nonlinear least squares regression model.

[0091] Among them, the parameter identification model is a nonlinear least squares regression model; the nonlinear least squares regression model can be applied to the complex domain impedance spectrum; based on the medium and low frequency equivalent circuit model structure, the complex domain impedance spectrum combined with the nonlinear least squares regression model, the medium and low frequency equivalent circuit model parameters can be determined as follows: the first resistance parameter R0 of the first resistor, the second resistance parameter R1 of the second resistor, the first capacitance parameter Q of the first solid-state diffusion impedance CPE1 (jw) and the first scale parameter α CPE1 , the second capacitance parameter Q of the second solid diffusion impedance CPE2 (jw) and the second proportional parameter α CPE2 .

[0092] S260, extracting medium and low frequency equivalent circuit model associated parameters from the medium and low frequency equivalent circuit model parameters.

[0093] Among them, the correlation parameters of the medium and low frequency equivalent circuit model are parameters with a high degree of correlation between factors that measure the similarity or difference in the development trends between each medium and low frequency equivalent circuit model parameter and the energy storage battery SoH parameter; the method for extracting the correlation parameters of the medium and low frequency equivalent circuit model is: obtaining the historical medium and low frequency equivalent circuit model parameters; performing correlation processing on the historical medium and low frequency equivalent circuit model parameters and each prior energy storage battery SoH parameter to obtain the correlation parameters of the medium and low frequency equivalent circuit model. The degree of correlation between the historical medium and low frequency equivalent circuit model parameters and each prior energy storage battery SoH parameter can be represented by a correlation coefficient; the correlation coefficient ξ i (k) is:

[0094]

[0095] In the formula, x0(k) is the reference sequence of SoH parameters of energy storage batteries under various historical ambient temperatures and battery capacities, and x i (k) is the parameter comparison sequence of each medium and low frequency equivalent circuit model under each historical ambient temperature and battery power; min i mix k |x0(k)-x i (k)| represents the minimum absolute value of the two sequences, max i max k |x0(k)-x i(k)| represents the maximum absolute value of the two sequences, and ρ is the resolution coefficient, which is usually taken as 0.5.

[0096] Considering that energy storage batteries involve several charge and discharge cycles in actual operation, the correlation r i Further calculations are as follows:

[0097]

[0098] In the formula, n represents the number of charge and discharge cycles of the energy storage battery. In the charge and discharge cycle, the higher the degree of synchronization of the two parameters, the closer ri is to 1. After calculation, the parameter with the highest correlation among the parameters of the medium and low frequency equivalent circuit models under the historical battery power is the first capacitance parameter Q of the first solid diffusion impedance. CPE1 (jw), the second capacitance parameter Q of the second solid-state diffusion impedance CPE2 (jw) and the second proportional parameter α CPE2 Therefore, the extracted medium and low frequency equivalent circuit model associated parameters are the first capacitance parameter Q of the first solid diffusion impedance CPE1 (jw), the second capacitance parameter Q of the second solid-state diffusion impedance CPE2 (jw) and the second proportional parameter α CPE2 .

[0099] S270, inputting the associated parameters of the medium and low frequency equivalent circuit models, the current temperature parameters and the current battery power SOC parameters into the neural network model to determine the SoH of the energy storage battery.

[0100] Among them, the neural network model includes a gated recurrent unit neural network; before determining the current energy storage battery SoH, first determine the trained gated recurrent unit neural network model, the gated recurrent unit neural network model may include an input layer, a GRU layer, a fully connected layer and an output layer; each neuron in the GRU layer can perform the following calculations:

[0101]

[0102] In the formula, Xt is the input vector feature, Zt and rt represent the update gate and reset gate at time t, Wz, Wr, W h are the input vector weight matrices, The update information that can be selected at time t;

[0103] In order to predict the SoH of the energy storage battery in the current cycle, a fully connected layer is used to transform the hidden state into the expected output:

[0104] SoH=Wh t +b

[0105] In the above formula, W and b are the weights and biases of the fully connected layer, and SoH is the health state of the energy storage battery in the current cycle;

[0106] Then, according to the correlation parameters of the medium and low frequency equivalent circuit model, the current ambient temperature parameters and the current battery power SOC parameters, the energy storage battery health characteristics Xi under the current cycle i are constructed. CPE1i , α CPE2i , Q CPE2i ,T i ,soc,], where the health characteristics are: the first capacitance parameter Q CPE1 (jw), the second capacitance parameter Q CPE2 (jw), the second scale parameter α CPE2 , the current ambient temperature and the current battery charge SOC, and input the health characteristics of each energy storage battery into the trained gated recurrent unit neural network model to determine the current energy storage battery SoH.

[0107] In this embodiment, the impedance spectrum is preprocessed to obtain a complex domain impedance spectrum, a full-frequency equivalent circuit model structure is constructed according to the complex domain impedance spectrum, a medium- and low-frequency equivalent circuit model structure in the full-frequency equivalent circuit model structure is extracted, and medium- and low-frequency equivalent circuit model parameters are determined based on the medium- and low-frequency equivalent circuit model structure, the complex domain impedance spectrum and a nonlinear least squares regression model, and medium- and low-frequency equivalent circuit model associated parameters are extracted from the medium- and low-frequency equivalent circuit model parameters. Since the extracted medium- and low-frequency equivalent circuit model associated parameters are highly correlated with the energy storage battery SoH, the medium- and low-frequency equivalent circuit model associated parameters, current temperature parameters and current battery power SOC parameters are input into the neural network model to further achieve accurate prediction of the energy storage battery SoH, thereby greatly improving the speed of predicting the energy storage battery SoH.

[0108] The energy storage battery SoH detection device provided in the embodiment of the present invention can execute the energy storage battery SoH detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. Figure 6 is a structural schematic diagram of a SoH detection device for an energy storage battery provided by an embodiment of the present invention, such as Figure 6 As shown, the device comprises:

[0109] The impedance spectrum determination module 10 is used to determine the impedance spectrum under different ambient temperatures and different battery power SOCs;

[0110] A circuit model building module 20, used to build a full-frequency equivalent circuit model structure according to the impedance spectrum;

[0111] An extraction module 30, used to extract a medium- and low-frequency equivalent circuit model structure from a full-frequency equivalent circuit model structure;

[0112] A circuit model parameter determination module 40 is used to determine the parameters of the medium and low frequency equivalent circuit model based on the medium and low frequency equivalent circuit model structure and the impedance spectrum combined with a preset parameter identification model;

[0113] The energy storage battery SoH determination module 50 is used to input the medium and low frequency equivalent circuit model parameters, the current ambient temperature parameters and the current battery power SOC parameters into the neural network model to determine the energy storage battery SoH.

[0114] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 7 As shown, the device includes a processor 70, a memory 71, an input device 72 and an output device 73; the number of processors 70 in the device can be one or more. Figure 7 A processor 70 is taken as an example; the processor 70, the memory 71, the input device 72 and the output device 73 in the device can be connected by a bus or other means. Figure 7 The example of connecting through bus is taken in the following.

[0115] The memory 71 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the energy storage battery SoH detection method in the embodiment of the present invention. The processor 70 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 71, that is, realizing the above-mentioned energy storage battery SoH detection method.

[0116] The memory 71 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 71 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 71 may further include a memory remotely arranged relative to the processor 70, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0117] The input device 72 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 73 may include a display device such as a display screen.

[0118] An embodiment of the present invention further provides a storage medium containing computer executable instructions, wherein the computer executable instructions are used to execute a method for detecting SoH of an energy storage battery when executed by a computer processor, the method comprising:

[0119] Determine the impedance spectrum under different ambient temperatures and different battery charge SOC;

[0120] Constructing a full-frequency equivalent circuit model structure according to the impedance spectrum;

[0121] Extracting a medium- and low-frequency equivalent circuit model structure from the full-frequency equivalent circuit model structure;

[0122] Determine medium and low frequency equivalent circuit model parameters based on the medium and low frequency equivalent circuit model structure, the impedance spectrum and a preset parameter identification model;

[0123] The medium and low frequency equivalent circuit model parameters, current temperature parameters and current battery power SOC parameters are input into the neural network model to determine the energy storage battery SoH.

[0124] Of course, the computer executable instructions of a storage medium containing computer executable instructions provided in an embodiment of the present invention are not limited to the operations of the method described above, and can also execute related operations in the energy storage battery SoH detection method provided in any embodiment of the present invention.

[0125] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0126] It is worth noting that in the embodiment of the above-mentioned search device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0127] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for detecting SoH of an energy storage battery, characterized in that: include: Determine the impedance spectrum under the current ambient temperature and the current battery charge SOC; Constructing a full-frequency equivalent circuit model structure according to the impedance spectrum; Extracting a medium- and low-frequency equivalent circuit model structure from the full-frequency equivalent circuit model structure; Determine medium and low frequency equivalent circuit model parameters based on the medium and low frequency equivalent circuit model structure, the impedance spectrum and a preset parameter identification model; Inputting the medium and low frequency equivalent circuit model parameters, the current ambient temperature parameters and the current battery power SOC parameters into the neural network model to determine the energy storage battery SoH; Determine the impedance spectrum under the current ambient temperature and current battery charge SOC, including: An impedance spectrum excitation circuit is set to output excitation signals of different frequencies; wherein the impedance spectrum excitation circuit includes a preset number of shift registers and XOR gates; Under the excitation signals of different frequencies, the current value and voltage value of the energy storage battery to be tested under the current ambient temperature and the current battery power SOC are obtained; Determine the impedance spectrum under the current ambient temperature and the current battery power SOC according to the excitation signals of different frequencies, the current ambient temperature and the current battery power SOC of the energy storage battery to be tested and the voltage value; The parameter identification model is a nonlinear least squares regression model; The energy storage battery SoH detection method also includes: Preprocessing the impedance spectrum to obtain a complex domain impedance spectrum; A full-frequency equivalent circuit model structure is constructed according to the impedance spectrum, including: Construct a full-frequency equivalent circuit model structure according to the complex domain impedance spectrum, Determining medium and low frequency equivalent circuit model parameters based on the medium and low frequency equivalent circuit model structure and the impedance spectrum in combination with a preset parameter identification model includes: Determine medium and low frequency equivalent circuit model parameters based on the medium and low frequency equivalent circuit model structure, the complex domain impedance spectrum and the nonlinear least squares regression model; Also includes: Extracting medium and low frequency equivalent circuit model associated parameters from the medium and low frequency equivalent circuit model parameters; Inputting the medium and low frequency equivalent circuit model parameters, the current ambient temperature parameters and the current battery power SOC parameters into the neural network model to determine the energy storage battery SoH, including: Inputting the medium and low frequency equivalent circuit model associated parameters, current ambient temperature parameters and current battery power SOC parameters into the neural network model to determine the energy storage battery SoH; The medium and low frequency equivalent circuit model structure includes: a first resistor, a second resistor, a first solid-state diffusion impedance and a second solid-state diffusion impedance; Determining medium and low frequency equivalent circuit model parameters based on the medium and low frequency equivalent circuit model structure and the impedance spectrum in combination with a preset parameter identification model includes: Based on the medium and low frequency equivalent circuit model structure, the complex domain impedance spectrum and a preset parameter identification model, a first resistance parameter of the first resistor, a second resistance parameter of the second resistor, a first capacitance parameter and a first proportional parameter of the first solid-state diffusion impedance, and a second capacitance parameter and a second proportional parameter of the second solid-state diffusion impedance are determined.

2. The energy storage battery SoH detection method according to claim 1, characterized in that: The second resistor is electrically connected in parallel with the first solid-state diffusion impedance, the first resistor and the second resistor are electrically connected in series, and the second solid-state diffusion impedance is electrically connected in series with the first solid-state diffusion impedance.

3. The energy storage battery SoH detection method according to claim 2, characterized in that: Extract the medium and low frequency equivalent circuit model associated parameters from the medium and low frequency equivalent circuit model parameters, including: Extract the first resistance parameter of the first resistor, the second resistance parameter of the second resistor, the first capacitance parameter and the first proportional parameter of the first solid-state diffusion impedance, the second capacitance parameter and the second proportional parameter of the second solid-state diffusion impedance.

4. The energy storage battery SoH detection method according to claim 1, characterized in that: The neural network model includes a gated recurrent unit neural network; The energy storage battery SoH detection method further includes: The gated recurrent unit neural network model is determined according to historical low-frequency equivalent circuit model parameters, different ambient temperature parameters and different battery power SOC parameters.

5. A SoH detection device for an energy storage battery, which is controlled by the SoH detection method for an energy storage battery according to any one of claims 1 to 4, characterized in that: include: An impedance spectrum determination module is used to determine the impedance spectrum under the current ambient temperature and the current battery power SOC; A circuit model building module, used to build a full-frequency equivalent circuit model structure according to the impedance spectrum; An extraction module, used to extract the medium and low frequency equivalent circuit model structure in the full frequency equivalent circuit model structure; A circuit model parameter determination module, used to determine the medium and low frequency equivalent circuit model parameters based on the medium and low frequency equivalent circuit model structure, the impedance spectrum and a preset parameter identification model; The energy storage battery SoH determination module is used to input the medium and low frequency equivalent circuit model parameters, the current ambient temperature parameters and the current battery power SOC parameters into the neural network model to determine the energy storage battery SoH.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the energy storage battery SoH detection method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the energy storage battery SoH detection method according to any one of claims 1 to 4 when executed.

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