Battery internal information detection modeling method, device and system based on impedance spectrum detection

Abstract

The invention discloses a battery internal information detection modeling method, device and system based on impedance spectrum detection, and belongs to the technical field of batteries. The method comprises the following steps: acquiring voltage data and current data of each battery in a battery system under characteristic impedance frequency alternating current; alternating current sine components in the voltage data and the current data are extracted, and a battery impedance spectrum is obtained through calculation; establishing an impedance expression based on an equivalent circuit model of the battery, and selecting N1 data points on a high-frequency-band graphic arc of a battery impedance spectrum to obtain a variance sum of a fitting arc; performing iterative calculation by adopting a nonlinear least square method to obtain each equivalent circuit parameter; respectively constructing a relation model of each equivalent circuit parameter and the internal parameter information of the battery; and calculating internal parameter information of the battery according to the relation model in combination with an engineering empirical coefficient, and detecting and analyzing the internal information of the battery in real time. The online real-time detection of the impedance spectrum data of the battery and the modeling analysis of the internal state parameters of the battery based on the impedance spectrum data are realized.

Description

Technical Field

[0001] The present invention belongs to the field of battery technology, and more specifically, relates to a battery internal information detection modeling method, device and system based on impedance spectrum detection. Background Art

[0002] Studying the internal reaction process and modeling methods of batteries helps to deeply understand the characteristics of batteries, and has important guiding significance for battery fault warning, state estimation and battery system management. However, the battery material system is complex, the interface reaction is changeable, and the failure mode is difficult to predict. The existing battery management technology mainly relies on the detection and analysis of battery external characteristic parameters. It is difficult to detect the internal process and parameters of the battery online, and it is difficult to support the needs of battery safety management and state estimation.

[0003] Various battery analysis and testing technologies can be used to analyze and study processes such as charge transfer and ion transport at the electrode / electrolyte interface, providing guidance for maintaining stable battery cycles. However, traditional non-in-situ characterization techniques require disassembling the battery and have a time lag. It is usually difficult to detect the real-time reaction process inside the battery and the information of the internal components of the battery under the actual working environment, and has gradually failed to meet the actual needs of research and engineering. Therefore, the development of new in-situ, non-destructive, and online detection technologies that can accurately characterize the internal processes of the battery has become a research hotspot in the field of electrochemical energy storage in recent years.

[0004] Compared with non-in-situ testing, in-situ analytical detection technology refers to non-destructive testing of batteries during battery operation. Without disassembling the battery, the internal information of the battery can be obtained in real time, which is helpful to study the evolution of the interface in the electrochemical process of the battery and analyze the complex relationship between electrode material behavior and battery performance in different time dimensions, thereby deeply analyzing the battery aging mechanism and clarifying the key issues of service and failure.

[0005] Electrochemical impedance spectroscopy (EIS) is a very important detection method in the study of lithium-ion battery structure and state, and has been widely used in the analysis of positive and negative electrode materials, research on lithium-ion intercalation and deintercalation kinetic parameters, solid electrolytes, interface reactions, SOC prediction and safety warning.

[0006] The existing technology lacks a solution to integrate the battery impedance spectrum detection and analysis system into the existing battery management system, making it difficult to effectively realize online detection and modeling analysis of the internal information of the battery based on the impedance spectrum online detection data. Summary of the invention

[0007] In view of the defects of the related art, the purpose of the present invention is to provide a battery internal information detection modeling method, device and system based on impedance spectrum detection, aiming to solve the problem that the prior art cannot integrate the battery impedance spectrum detection and analysis system into the existing battery management system and realize online modeling and analysis of the battery internal information.

[0008] To achieve the above objectives, in a first aspect, the present invention provides a battery internal information detection modeling method based on impedance spectrum detection, comprising:

[0009] S1. Obtaining voltage data and current data of each battery in the battery system under characteristic impedance frequency alternating current;

[0010] S2, extracting the AC sinusoidal component at the characteristic impedance frequency from the voltage data and the current data, constructing an impedance vector matrix, and calculating the battery impedance spectrum;

[0011] S3. Establish an impedance expression based on an equivalent circuit model of the battery, select N1 data points on the high-frequency band arc of the battery impedance spectrum, and obtain the sum of variances of the N1 data points to the fitting arc; and iteratively calculate the sum of variances using a nonlinear least squares method to obtain each equivalent circuit parameter in the equivalent circuit model;

[0012] S4. According to each equivalent circuit parameter in the equivalent circuit model, a relationship model of the ohmic internal resistance R0, the double-layer capacitance reactance C, the Faraday impedance R1 of the transfer process and the internal parameter information of the battery is constructed respectively;

[0013] S5. Calculate the internal parameter information of the battery according to the relationship model combined with the engineering experience coefficient, and perform real-time detection and analysis on the internal information of the battery.

[0014] Optionally, S4 specifically includes:

[0015] Construct a relationship model between the ohmic internal resistance R0 and the internal parameter information of the battery:

[0016]

[0017] Among them, ε S is the porous electrode porosity, σ S is the conductivity of the solid electrode material, ε l is the lithium ion equivalent mobility, δ l is the electrolyte conductivity, c l is the electrolyte lithium ion movement speed, T is the temperature;

[0018] Construct a relationship model between the double-layer capacitor reactance C and the internal parameter information of the battery:

[0019]

[0020] Among them, C inn is the fixed layer capacitance, R is the gas constant, F is the Faraday constant, T is the temperature, c s is the ion concentration, ε0 and ε r is the dielectric constant on both sides of the electrode interface, It is the average potential at a certain distance from the electrode surface;

[0021] Construct a relationship model between the Faraday impedance R1 of the transfer process and the internal parameter information of the battery:

[0022]

[0023] Where R is the gas constant, F is the Faraday constant, T is the temperature, c s,max is the nominal capacity of the battery, k0 is the electrochemical reaction rate constant, α a is the anode transfer coefficient, α c is the cathode transfer coefficient, c l is the electrolyte lithium ion movement speed, and SOC is the battery state of charge.

[0024] Optionally, S1 specifically includes:

[0025] S11. According to the impedance frequency point f required for battery impedance spectrum measurement x , applying a frequency of f to the battery system x of alternating current;

[0026] S12, obtaining voltage data and current data of each battery in the battery system;

[0027] S13, determining whether the voltage data and current data at all frequencies required for calculating the battery impedance spectrum are obtained, if so, executing step S2, if not, returning to executing step S11.

[0028] Optionally, S2 specifically includes:

[0029] S21, extracting the AC sinusoidal component U(f) at the characteristic impedance frequency from the voltage data and the current data x ) and I(f x ), f x ,x∈[1,2,…,n];

[0030] S22, constructing a current vector matrix I and a voltage vector matrix U respectively, and constructing an impedance vector matrix Z according to the current vector matrix I and the voltage vector matrix U, and calculating and obtaining a battery impedance spectrum.

[0031] Optionally, S3 specifically includes:

[0032] S31, establish an equivalent circuit model of the battery, wherein the equivalent circuit structure is that C is connected in parallel with R1 and then connected in series with R0, and establish an impedance expression:

[0033]

[0034] S32, select N1 data points on the high frequency arc of the battery impedance spectrum, set the center of the arc to (x0, y0), the radius to r0, the kth data point to (R k ,X k ), and get the sum of the variances of the N1 data points for the fitted arc:

[0035]

[0036] S33. According to the nonlinear least squares principle, set the intermediate parameters to be determined, solve the variance and iteratively to obtain the intermediate parameters, and then obtain the equivalent circuit parameters according to the relationship between the intermediate parameters and the equivalent circuit parameters.

[0037] In a second aspect, the present invention further provides a battery internal information detection modeling device based on impedance spectrum detection, comprising:

[0038] A data acquisition module, used to acquire voltage data and current data of each battery in the battery system under characteristic impedance frequency alternating current;

[0039] A battery impedance spectrum calculation module is used to extract the AC sinusoidal component at the characteristic impedance frequency in the voltage data and the current data, construct an impedance vector matrix, and calculate the battery impedance spectrum;

[0040] An equivalent circuit parameter calculation module is used to establish an impedance expression based on an equivalent circuit model of a battery, select N1 data points on a high-frequency band arc of the battery impedance spectrum, and obtain the sum of variances of the N1 data points to the fitting arc; and iteratively calculate the sum of variances using a nonlinear least squares method to obtain each equivalent circuit parameter in the equivalent circuit model;

[0041] A model building module, used to build a relationship model between the ohmic internal resistance R0, the double-layer capacitance reactance C, the Faraday impedance R1 of the transfer process and the internal parameter information of the battery according to each equivalent circuit parameter in the equivalent circuit model;

[0042] The battery information monitoring module is used to calculate the internal parameter information of the battery according to the relationship model combined with the engineering experience coefficient, and perform real-time detection and analysis of the internal information of the battery.

[0043] In a third aspect, the present invention also provides a battery internal information detection modeling system based on impedance spectrum detection, comprising: a processing system, a current generating branch and a battery system;

[0044] The processing system sends a control signal to the current generating branch to control it to apply alternating current of different frequencies to the battery system, and obtains voltage data and current data of each battery in the battery system, and executes the battery internal information detection modeling method based on impedance spectrum detection as described in any one of the first aspects.

[0045] Optionally, the current generating branch includes a semiconductor switch tube Q, a current limiting resistor R s , inductor L and freewheeling diode D;

[0046] The current input end of the semiconductor switch tube Q is connected to the positive electrode of the battery system, and the current output end is connected to the current limiting resistor R s The first end of the current limiting resistor R s The second end of the inductor L is connected to the first end of the inductor L, the second end of the inductor L is connected to the negative electrode of the battery system and the positive electrode of the freewheeling diode D, and the negative electrode of the freewheeling diode D is connected to the current limiting resistor R s The first end of

[0047] The control instructions of the control system control the on and off of the semiconductor switch tube Q.

[0048] Compared with the prior art, the above technical solution conceived by the present invention can achieve the following beneficial effects:

[0049] 1. The present invention provides a battery internal information detection modeling method based on impedance spectrum detection. The battery impedance spectrum is calculated by the voltage data and current data of each battery in the battery system under different frequency alternating current, and the battery impedance spectrum is combined with the equivalent circuit model of the battery. The variance sum of the high-frequency band graphic arc of the battery impedance spectrum is constructed by using graphic parameters, and each equivalent circuit parameter in the equivalent circuit model is iteratively calculated by nonlinear least squares method, so as to construct a relationship model between each equivalent circuit parameter and the internal parameter information of the battery. The characterization calculation of the internal parameter information of the battery represented by the temperature inside the battery is realized by combining the impedance spectrum detection analysis results with the battery material coefficient and the engineering experience coefficient, so as to realize the real-time detection and analysis of the internal state of the battery. Compared with the prior art, the test data of the battery impedance spectrum, that is, the voltage data and current data under different frequency alternating current, can be obtained online and in real time, and the battery impedance spectrum detection and analysis system is integrated into the existing battery management system, effectively realizing the online detection and modeling analysis of the internal information of the battery based on the online real-time detection data of the impedance spectrum.

[0050] 2. The present invention provides a battery internal information detection and modeling system based on impedance spectrum detection. There is no need to modify the circuit structure of the existing battery management system. The excitation current at different frequencies required for impedance spectrum monitoring can be generated through a simple and reliable parallel branch circuit; the battery impedance spectrum detection and modeling are realized through the multi-level sampling calculation of the host computer system. The disturbance current generation branch structure is simple and effective, and the engineering implementation cost is low; the battery management unit and battery cluster management unit provided in the battery system are used to perform voltage and current detection, and are reused with conventional voltage and current detection functions to achieve cost reduction and efficiency improvement. Compared with the prior art, this solution can effectively realize online detection and modeling analysis of battery internal information based on impedance spectrum online detection data through a small amount of modification in the existing battery system, and reduce costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of a battery internal information detection modeling method based on impedance spectrum detection provided by the present invention;

[0052] Figure 2 It is a structural schematic diagram of a battery internal information detection modeling system based on impedance spectrum detection provided by the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0054] The contents involved in the above embodiment are described below in conjunction with a preferred embodiment.

[0055] Embodiment 1

[0056] The present invention provides a battery internal information detection modeling method based on impedance spectrum detection, comprising:

[0057] S1. Obtaining voltage data and current data of each battery in the battery system under characteristic impedance frequency alternating current;

[0058] S2, extracting the AC sinusoidal component at the characteristic impedance frequency from the voltage data and the current data, constructing an impedance vector matrix, and calculating the battery impedance spectrum;

[0059] S3. Establish an impedance expression based on an equivalent circuit model of the battery, select N1 data points on the high-frequency band arc of the battery impedance spectrum, and obtain the sum of variances of the N1 data points to the fitting arc; and iteratively calculate the sum of variances using a nonlinear least squares method to obtain each equivalent circuit parameter in the equivalent circuit model;

[0060] S4. According to each equivalent circuit parameter in the equivalent circuit model, a relationship model of the ohmic internal resistance R0, the double-layer capacitance reactance C, the Faraday impedance R1 of the transfer process and the internal parameter information of the battery is constructed respectively;

[0061] S5. Calculate the internal parameter information of the battery according to the relationship model combined with the engineering experience coefficient, and perform real-time detection and analysis on the internal information of the battery.

[0062] refer to Figure 1 and Figure 2 The processing system includes a host computer system and a control system. The host computer system sends a general control instruction and displays the results of the battery impedance spectrum and the battery internal information modeling and analysis. The control system controls the disturbance current generating branch connected in parallel with the battery system to generate AC currents of different frequencies required for impedance spectrum measurement, and collects battery voltage and current data through the battery management system. An impedance vector matrix is ​​constructed in the control system to calculate the battery impedance spectrum and upload the calculation results to the host computer system. In the host computer system, based on the established battery equivalent circuit model and impedance spectrum data, the variance sum of the high-frequency graphic arc of the battery impedance spectrum is constructed using graphic parameters, and the nonlinear least squares method is used to iteratively calculate the equivalent circuit parameters in the equivalent circuit model, specifically including the equivalent circuit parameters that characterize the battery ohmic internal resistance, charge transfer impedance, and double-layer capacitor reactance. Then, a relationship model is constructed based on the constructed battery equivalent circuit parameters and the battery internal parameter information to realize real-time detection and analysis of the battery internal information.

[0063] Optionally, S1 specifically includes:

[0064] S11. According to the impedance frequency point f required for battery impedance spectrum measurement x , applying a frequency of f to the battery system x of alternating current;

[0065] S12, obtaining voltage data and current data of each battery in the battery system;

[0066] S13, determining whether the voltage data and current data at all frequencies required for calculating the battery impedance spectrum are obtained, if so, executing step S2, if not, returning to executing step S11.

[0067] Determine the impedance frequency point f to be measured according to the impedance spectrum measurement requirements xThe host computer system sends the corresponding control instructions to the control system, and the control system outputs the control signal to control the semiconductor switch tube Q in the current branch at a frequency f x Turn on and off, and apply AC current of corresponding frequency to the battery system. The battery management unit and battery cluster management unit in the battery system detect the battery voltage data and battery current data, and upload them to the control system. The control system determines whether the voltage and current data measurement at all frequencies required for the battery impedance spectrum calculation is completed. If it is completed, it will proceed to the next step. If it is not completed, it will return to continue to select a new impedance frequency point f x And repeat the steps to obtain voltage and current data.

[0068] Optionally, S2 specifically includes:

[0069] S21, extracting the AC sinusoidal component U(f) at the characteristic impedance frequency from the voltage data and the current data x ) and I(f x ), f x ,x∈[1,2,…,n];

[0070] S22, constructing a current vector matrix I and a voltage vector matrix U respectively, and constructing an impedance vector matrix Z according to the current vector matrix I and the voltage vector matrix U, and calculating and obtaining a battery impedance spectrum.

[0071] f x , x∈[1,2,…,n] is the frequency of each AC current generated by the disturbance current generating branch on the battery system. Construct the current vector matrix I, voltage vector matrix U and impedance vector matrix Z. The matrix expressions are as follows:

[0072]

[0073] According to Ohm's law, we can get:

[0074] Z=UI -1 (4)

[0075] In the formula, I -1 is the inverse matrix of I. The calculation formula for each item in the matrix formula is as follows:

[0076] Z(f x )=U(f x )I(f x ) -1 ,x∈[1,2,…,n] (5)

[0077] The voltage can be described as:

[0078] U(f x )=|U(f x )|cos(2πf xt)=Re{U(f x )exp(j2πf x t)},x∈[1,2,…,n] (6)

[0079] Where t is the amount of time and the current can be expressed as:

[0080]

[0081] in, is the impedance phase angle. According to formula (4), the impedance expression can be obtained as follows:

[0082]

[0083] Plotting the impedance on the complex plane, with the ordinate taking a negative value, it can be expressed as:

[0084]

[0085] At this point, the real and imaginary parts of the impedance can be expressed as:

[0086]

[0087] Therefore, after calculating the real and imaginary parts of the impedance, the impedance phase angle and modulus can be expressed as:

[0088]

[0089] According to formula (3-3), the impedance vector matrix can be obtained as:

[0090]

[0091] The phase angle matrix is:

[0092]

[0093] The impedance modulus matrix is:

[0094]

[0095] Therefore, the required battery impedance spectrum data can be calculated through the extracted voltage and current data.

[0096] Optionally, S3 specifically includes:

[0097] S31, establish an equivalent circuit model of the battery, wherein the equivalent circuit structure is that C is connected in parallel with R1 and then connected in series with R0, and establish an impedance expression:

[0098]

[0099] S32, select N1 data points on the high frequency arc of the battery impedance spectrum, set the center of the arc to (x0, y0), the radius to r0, the kth data point to (R k ,X k ), and get the sum of the variances of the N1 data points for the fitted arc:

[0100]

[0101] S33. According to the nonlinear least squares principle, set the intermediate parameters to be determined, solve the variance and iteratively to obtain the intermediate parameters, and then obtain the equivalent circuit parameters according to the relationship between the intermediate parameters and the equivalent circuit parameters.

[0102] According to the nonlinear least squares principle, by setting the intermediate parameters x, y and r to be solved, we can get the linear equation system about x, y and r:

[0103] N1x+a0r=b0

[0104] N1y+a1r=b1

[0105] N1r+a0x+a1y=b2 (21)

[0106] Where:

[0107]

[0108] Among them, x, y and r are the parameters to be determined, the center coordinates (x0, y0) and the radius r0 are the initial values ​​determined, and iterative calculation is required until [(r-r0) / r] < 10 -4 When it ends, x, y and r are obtained through iterative calculation, and then R0, R1 and n1 are determined by the following formula:

[0109]

[0110] Substituting the obtained R0, R1 and n1 into formula (17) yields:

[0111]

[0112] Taking the logarithm of both sides squared, we get:

[0113] lg|Y|=lgY 01 +n1lgω (27)

[0114] According to equations (17), (26), (27) and the values ​​of R0 and R1, Y can be obtained 01 The value of , and then find the parameters of C.

[0115] Then, the parameters of the constructed battery equivalent circuit model were solved, including R0 representing the ohmic internal resistance, C representing the double-layer capacitance reactance, and R1 representing the charge transfer impedance.

[0116] Optionally, S4 specifically includes:

[0117] Construct a relationship model between the ohmic internal resistance R0 and the internal parameter information of the battery:

[0118]

[0119] Among them, ε S is the porous electrode porosity, σ S is the conductivity of the solid electrode material, ε l is the lithium ion equivalent mobility, δ l is the electrolyte conductivity, c l is the electrolyte lithium ion movement speed, T is the temperature;

[0120] Construct a relationship model between the double-layer capacitor reactance C and the internal parameter information of the battery:

[0121]

[0122] Among them, C inn is the fixed layer capacitance, R is the gas constant, F is the Faraday constant, T is the temperature, c s is the ion concentration, ε0 and ε r is the dielectric constant on both sides of the electrode interface, It is the average potential at a certain distance from the electrode surface;

[0123] Construct a relationship model between the Faraday impedance R1 of the transfer process and the internal parameter information of the battery:

[0124]

[0125] Where R is the gas constant, F is the Faraday constant, T is the temperature, c s,max is the nominal capacity of the battery, k0 is the electrochemical reaction rate constant, α a is the anode transfer coefficient, α c is the cathode transfer coefficient, c l is the electrolyte lithium ion movement speed, and SOC is the battery state of charge.

[0126] Based on the relationship model between R0, C and R1 and the internal parameter information of the battery, the internal parameter information of the battery represented by the temperature inside the battery can be characterized and calculated by combining the parameter identification values ​​of R0, C and R1 in the equivalent circuit model with the battery material coefficient and the engineering experience coefficient value.

[0127] In actual engineering applications, impedance spectrum detection and calculation are first performed, and then the battery equivalent circuit model parameters are identified and calculated based on the impedance spectrum detection results. Finally, several equivalent circuit model parameters and battery internal parameter information relationship models are integrated to obtain the desired battery internal parameter information by solving equations simultaneously. Based on this method, the internal temperature and SOC of the battery can be monitored online. When the battery temperature and SOC are known, the internal material coefficient of the battery can be obtained to determine the degree of battery aging.

[0128] The embodiment of the present invention provides a battery internal information detection modeling method based on impedance spectrum detection. The battery impedance spectrum is calculated through the voltage data and current data of each battery in the battery system under different frequency alternating current, and the battery impedance spectrum is combined with the equivalent circuit model of the battery to construct a relationship model between each equivalent circuit parameter and the battery internal parameter information. The impedance spectrum detection analysis results are combined with the battery material coefficient and the engineering experience coefficient to achieve the characterization calculation of the battery internal parameter information represented by the temperature inside the battery, thereby achieving real-time detection and analysis of the internal state of the battery. The problem that the existing technology cannot integrate the battery impedance spectrum detection and analysis system into the existing battery management system and realize online modeling and analysis of the battery internal information is solved. The battery impedance spectrum detection and analysis system is integrated into the existing battery management system to effectively realize online detection and modeling analysis of the battery internal information based on the impedance spectrum online detection data.

[0129] Embodiment 2

[0130] In a second aspect, the present invention further provides a battery internal information detection modeling device based on impedance spectrum detection, comprising:

[0131] A data acquisition module, used to acquire voltage data and current data of each battery in the battery system under characteristic impedance frequency alternating current;

[0132] A battery impedance spectrum calculation module is used to extract the AC sinusoidal component at the characteristic impedance frequency in the voltage data and the current data, construct an impedance vector matrix, and calculate the battery impedance spectrum;

[0133] An equivalent circuit parameter calculation module is used to establish an impedance expression based on an equivalent circuit model of a battery, select N1 data points on a high-frequency band arc of the battery impedance spectrum, and obtain the sum of variances of the N1 data points to the fitting arc; and iteratively calculate the sum of variances using a nonlinear least squares method to obtain each equivalent circuit parameter in the equivalent circuit model;

[0134] A model building module, used to build a relationship model between the ohmic internal resistance R0, the double-layer capacitance reactance C, the Faraday impedance R1 of the transfer process and the internal parameter information of the battery according to each equivalent circuit parameter in the equivalent circuit model;

[0135] The battery information monitoring module is used to calculate the internal parameter information of the battery according to the relationship model combined with the engineering experience coefficient, and perform real-time detection and analysis of the internal information of the battery.

[0136] A battery internal information detection and modeling device based on impedance spectrum detection provided by an embodiment of the present invention is used to execute a battery internal information detection and modeling method based on impedance spectrum detection provided by any embodiment of the present invention, and has corresponding beneficial effects.

[0137] Embodiment 3

[0138] like Figure 2 As shown, the present invention also provides a battery internal information detection modeling system based on impedance spectrum detection, including: a processing system, a current generating branch and a battery system;

[0139] The processing system sends a control signal to the current generating branch to control it to apply alternating current of different frequencies to the battery system, and obtains voltage data and current data of each battery in the battery system, and executes the battery internal information detection modeling method based on impedance spectrum detection as described in any one of Example 1.

[0140] Optionally, the current generating branch includes a semiconductor switch tube Q, a current limiting resistor R s , inductor L and freewheeling diode D;

[0141] The current input end of the semiconductor switch tube Q is connected to the positive electrode of the battery system, and the current output end is connected to the current limiting resistor R s The first end of the current limiting resistor R s The second end of the inductor L is connected to the first end of the inductor L, the second end of the inductor L is connected to the negative electrode of the battery system and the positive electrode of the freewheeling diode D, and the negative electrode of the freewheeling diode D is connected to the current limiting resistor R s The first end of

[0142] The control instructions of the control system control the on and off of the semiconductor switch tube Q.

[0143] This embodiment does not require modification of the circuit structure of the existing battery management system, and can generate excitation currents at different frequencies required for impedance spectrum monitoring through a simple and reliable parallel branch circuit. The disturbance current generating branch connected in parallel with the battery system is used to generate the AC current required for impedance spectrum measurement, and the control system generates a control signal for the semiconductor switch tube Q, so that the semiconductor switch tube Q is turned on and off at the same frequency as the characteristic impedance frequency to be detected, thereby generating the AC current required for impedance spectrum measurement on the battery system, and controlling the semiconductor switch tube Q to turn on and off at different frequencies to generate AC currents of different frequencies on the battery system, so as to perform battery impedance spectrum measurement at multiple frequency points. In the positive half cycle when the semiconductor switch tube Q is turned on, the voltage at the battery system end is greater than the voltage at the inductor end, and the battery system discharges to the disturbance current branch. The current of the inductor L gradually increases under the action of the battery voltage. At this time, the freewheeling diode D is in a closed state due to the negative voltage. In the negative half cycle when the semiconductor switch tube Q is turned off, the current flowing through the inductor L forms a freewheeling loop through the freewheeling diode D, which gradually reduces the inductor current and avoids the spike voltage caused by the sudden change of the inductor current to damage the circuit. The current limiting resistor R s It plays the role of current limiting and protection. The structure of the disturbance current generating branch is simple and effective, and the engineering implementation cost is low.

[0144] Furthermore, while the disturbance current generating branch generates the AC current required for impedance spectrum measurement on the battery system, the battery voltage data is detected by the battery management unit (BMU) in the battery system, and the battery current data is detected by the battery cluster management unit (BCU) in the battery system. The battery voltage and current detection data are uploaded to the control system. The battery management unit (BMU) and battery cluster management unit (BCU) in the battery system are used to perform voltage and current detection, which can be reused with the conventional voltage and current detection functions to further reduce costs and increase efficiency.

[0145] The embodiment of the present invention provides a battery internal information detection modeling method based on impedance spectrum detection. The excitation current at different frequencies required for impedance spectrum monitoring can be generated through a simple and reliable parallel branch circuit. The disturbance current generation branch structure is simple and effective, and the engineering implementation cost is low. The battery management unit and battery cluster management unit provided in the battery system are used to perform voltage and current detection, which further saves system costs. The battery impedance spectrum detection and modeling are realized through multi-level sampling calculation of the host computer system, which solves the problem that the existing technology cannot integrate the battery impedance spectrum detection and analysis system into the existing battery management system at a low cost and realize the online modeling and analysis of the battery internal information. Through a small amount of modification, the online detection and modeling analysis of the battery internal information based on the impedance spectrum online detection data can be effectively realized, and the cost can be reduced.

[0146] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A battery internal information detection modeling method based on impedance spectrum detection, characterized in that: include: S1. Obtaining voltage data and current data of each battery in the battery system under characteristic impedance frequency alternating current; S2, extracting the AC sinusoidal component at the characteristic impedance frequency from the voltage data and the current data, constructing an impedance vector matrix, and calculating the battery impedance spectrum; S3. Establish an impedance expression based on an equivalent circuit model of the battery, select N1 data points on the high-frequency band arc of the battery impedance spectrum, and obtain the sum of variances of the N1 data points to the fitting arc; and iteratively calculate the sum of variances using a nonlinear least squares method to obtain each equivalent circuit parameter in the equivalent circuit model; S4. According to each equivalent circuit parameter in the equivalent circuit model, a relationship model of the ohmic internal resistance R0, the double-layer capacitance reactance C, the Faraday impedance R1 of the transfer process and the internal parameter information of the battery is constructed respectively; S5. Calculate the internal parameter information of the battery according to the relationship model combined with the engineering experience coefficient, and perform real-time detection and analysis on the internal information of the battery.

2. The method according to claim 1, characterized in that S4 specifically includes: Construct a relationship model between the ohmic internal resistance R0 and the internal parameter information of the battery: Among them, ε S is the porous electrode porosity, σ S is the conductivity of the solid electrode material, ε l is the lithium ion equivalent mobility, δ l is the electrolyte conductivity, c l is the electrolyte lithium ion movement speed, T is the temperature; Construct a relationship model between the double-layer capacitor reactance C and the internal parameter information of the battery: Among them, C inn is the fixed layer capacitance, R is the gas constant, F is the Faraday constant, T is the temperature, c s is the ion concentration, ε0 and ε r is the dielectric constant on both sides of the electrode interface, It is the average potential at a certain distance from the electrode surface; Construct a relationship model between the Faraday impedance R1 of the transfer process and the internal parameter information of the battery: Where R is the gas constant, F is the Faraday constant, T is the temperature, c s,max is the nominal capacity of the battery, k0 is the electrochemical reaction rate constant, α a is the anode transfer coefficient, α c is the cathode transfer coefficient, c l is the electrolyte lithium ion movement speed, and SOC is the battery state of charge.

3. The method according to claim 1, characterized in that S1 specifically includes: S11. According to the impedance frequency point f required for battery impedance spectrum measurement x , applying a frequency of f to the battery system x of alternating current; S12, obtaining voltage data and current data of each battery in the battery system; S13, determining whether the voltage data and current data at all frequencies required for calculating the battery impedance spectrum are obtained, if so, executing step S2, if not, returning to executing step S11.

4. The method according to claim 1, characterized in that S2 specifically includes: S21, extracting the AC sinusoidal component U(f) at the characteristic impedance frequency from the voltage data and the current data x ) and I(f x ), f x ,x∈[1,2,…,n]; S22, constructing a current vector matrix I and a voltage vector matrix U respectively, and constructing an impedance vector matrix Z according to the current vector matrix I and the voltage vector matrix U, and calculating and obtaining a battery impedance spectrum.

5. The method according to claim 1, characterized in that S3 specifically includes: S31, establish an equivalent circuit model of the battery, wherein the equivalent circuit structure is that C is connected in parallel with R1 and then connected in series with R0, and establish an impedance expression: S32, select N1 data points on the high frequency arc of the battery impedance spectrum, set the center of the arc to (x0, y0), the radius to r0, the kth data point to (R k ,X k ), and get the sum of the variances of the N1 data points for the fitted arc: S33. According to the nonlinear least squares principle, set the intermediate parameters to be determined, solve the variance and iteratively to obtain the intermediate parameters, and then obtain the equivalent circuit parameters according to the relationship between the intermediate parameters and the equivalent circuit parameters.

6. A battery internal information detection modeling device based on impedance spectrum detection, characterized in that: include: A data acquisition module, used to acquire voltage data and current data of each battery in the battery system under characteristic impedance frequency alternating current; A battery impedance spectrum calculation module is used to extract the AC sinusoidal component at the characteristic impedance frequency in the voltage data and the current data, construct an impedance vector matrix, and calculate the battery impedance spectrum; An equivalent circuit parameter calculation module is used to establish an impedance expression based on an equivalent circuit model of a battery, select N1 data points on a high-frequency band arc of the battery impedance spectrum, and obtain the sum of variances of the N1 data points to the fitting arc; and iteratively calculate the sum of variances using a nonlinear least squares method to obtain each equivalent circuit parameter in the equivalent circuit model; A model building module, used to build a relationship model between the ohmic internal resistance R0, the double-layer capacitance reactance C, the Faraday impedance R1 of the transfer process and the internal parameter information of the battery according to each equivalent circuit parameter in the equivalent circuit model; The battery information monitoring module is used to calculate the internal parameter information of the battery according to the relationship model combined with the engineering experience coefficient, and perform real-time detection and analysis of the internal information of the battery.

7. A battery internal information detection modeling system based on impedance spectrum detection, characterized in that: include: processing system, current generating branch and battery system; The processing system sends a control signal to the current generating branch to control it to apply alternating current of different frequencies to the battery system, and obtains voltage data and current data of each battery in the battery system, and executes the battery internal information detection modeling method based on impedance spectrum detection as described in any one of claims 1-5.

8. The system according to claim 7, characterized in that The current generating branch includes a semiconductor switch tube Q, a current limiting resistor R s , inductor L and freewheeling diode D; The current input end of the semiconductor switch tube Q is connected to the positive electrode of the battery system, and the current output end is connected to the current limiting resistor R s The first end of the current limiting resistor R s The second end of the inductor L is connected to the first end of the inductor L, the second end of the inductor L is connected to the negative electrode of the battery system and the positive electrode of the freewheeling diode D, and the negative electrode of the freewheeling diode D is connected to the current limiting resistor R s The first end of The control instructions of the control system control the on and off of the semiconductor switch tube Q.

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