Method and device for determining battery internal resistance

Abstract

The present invention discloses a method and device for determining the internal resistance of a battery. The method comprises: obtaining the measured potential and applied current of the battery, as well as the battery equilibrium potential of the battery under multiple charge states during the process of measuring the DC internal resistance; obtaining a simulated change curve of the battery potential through a potential equation of multiple overpotentials, battery equilibrium potential and battery potential; making the simulated change curve close to the measured change curve by changing the values ​​of multiple overpotentials, wherein the measured change curve is a change curve of the measured potential and time; determining the corresponding multiple overpotentials when the difference between the simulated change curve and the measured potential reaches a preset difference; determining multiple impedances of the battery according to the determined multiple overpotentials and applied currents. The present invention solves the technical problem that the detection accuracy is low and the detection is not comprehensive in the related art, that is, the method of detecting the battery by technical parameters.

Description

Technical Field

[0001] The present invention relates to the field of batteries, and in particular to a method and device for determining the internal resistance of a battery. Background Art

[0002] Battery systems based on the lithium-ion rocking chair mechanism have become the preferred technology for electric vehicle power cells for a long time in the future due to their high energy and power density and long service life. The direct current resistance (DCR) of power cells has a great influence on key performance parameters such as battery capacity, power output, heat generation and self-discharge. There are many factors that affect the size of DCR, such as the current collector, the resistance of the tab itself, the intrinsic and interfacial dynamics of the positive and negative electrodes, and the diffusion properties of lithium ions in the main material and electrolyte. If a method that can predict and decompose DCR can be established, it will play an important guiding role in the design of battery cells and modules, failure mechanisms, etc.

[0003] At present, there are two main methods for measuring DCR: the DC method and the AC method. One implementation method of the DC method is to apply a constant current pulse of a specific rate and time to the battery at a certain state of charge (SOC), record the voltage before charge and discharge (V1) and the charge and discharge termination voltage (V2), and divide the difference between the two voltages by the constant current (V1-V2 / I) to obtain the corresponding charge and discharge DC internal resistance.

[0004] One implementation method of measuring internal resistance by AC method is to use AC internal resistance meter to measure directly, which can only measure ohmic internal resistance. Another method is electrochemical impedance spectroscopy (EIS). EIS can measure the ohmic internal resistance and charge transfer internal resistance of the battery in a state of approximate equilibrium, but cannot measure the diffusion internal resistance. And when using EIS to measure AC impedance spectrum, extremely small voltage disturbance is required, so this method will not be able to measure the ohmic internal resistance and charge transfer internal resistance of the battery in actual dynamic use.

[0005] As a simulation software based on finite elements, COMSOL Multiphysics has received extensive attention in academia and industry due to its diversity of physical fields, convenient interface interaction, intelligent physical field coupling, and powerful computing power for partial differential equations. COMSOL Multiphysics can simulate various product designs and parameter optimization problems involved in engineering, manufacturing, and scientific research.

[0006] In the measurement practice of DCR, the DC method requires the application of high-rate current pulses, which increases the testing cost. And due to the accuracy limitations of the test machine and the individual differences of the battery cells, the decomposition of DCR (ohmic impedance, charge transfer impedance, concentration impedance) cannot reach a very accurate level. The AC law cannot fully measure the various impedances that make up DCR. In the prediction practice of DCR, the existing technology generally uses IT programming technologies with high technical barriers such as neural networks, machine learning, and cloud platforms. It is not friendly to small and medium-sized manufacturers, and the measurement investment cost is high, which limits its application scope.

[0007] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0008] The embodiments of the present invention provide a method and device for determining the internal resistance of a battery, so as to at least solve the technical problems of low detection accuracy and incomplete detection in the related art of detecting the battery by technical parameters.

[0009] According to one aspect of an embodiment of the present invention, a method for determining the internal resistance of a battery is provided, comprising: in a process of measuring the DC internal resistance, obtaining the measured potential and applied current of the battery, and the battery equilibrium potential of the battery at multiple charge states; obtaining a simulated change curve of the battery potential through multiple overpotentials, the battery equilibrium potential and a potential equation of the battery potential; bringing the simulated change curve close to the measured change curve by changing the values ​​of the multiple overpotentials, wherein the measured change curve is a change curve of the measured potential versus time; determining a corresponding multiple overpotentials when the difference between the simulated change curve and the measured change curve reaches a preset difference; and determining multiple impedances of the battery based on the determined multiple overpotentials and the applied current.

[0010] Optionally, the multiple overpotentials include an ohmic overpotential, an electrochemical reaction overpotential and a concentration overpotential; obtaining a simulated change curve of the battery potential through the multiple overpotentials, the battery equilibrium potential and the potential equation of the battery potential includes: obtaining the potential equation, the potential equation is Ecell=Eocv(SOCave,T)+ηIR+ηact+ηconc, wherein Ecell is the battery potential, Eocv(SOCave,T) is a function of the battery equilibrium potential with respect to the state of charge and temperature, ηIR is the ohmic overpotential, ηact is the electrochemical reaction overpotential, and ηconc is the concentration overpotential; setting initial empirical values ​​for the ohmic overpotential, the electrochemical reaction overpotential and the concentration overpotential, and obtaining the simulated change curve of the battery potential through multiple battery equilibrium potentials.

[0011] Optionally, before being applied to a model developer and obtaining a simulated change curve of the battery potential through a plurality of overpotentials, the battery equilibrium potential and the potential equation of the battery voltage, the method further includes: establishing a lumped battery physical field node corresponding to the battery in the model developer, wherein the lumped battery physical field node includes a battery equilibrium potential sub-node and a voltage loss sub-node; obtaining the plurality of overpotentials, the battery equilibrium potential and the potential equation of the battery potential through the lumped battery physical field node; generating a function of the battery equilibrium potential to the state of charge and the temperature according to the battery equilibrium potential under a plurality of states of charge and the corresponding temperatures through the battery balance positioning sub-node; and determining the plurality of overpotentials through the voltage loss sub-node.

[0012] Optionally, bringing the simulated change curve close to the measured change curve by changing the values ​​of the multiple overpotentials includes: establishing an optimization node, and changing the values ​​of the multiple overpotentials through the optimization node according to a least squares method to bring the simulated change curve close to the measured change curve.

[0013] Optionally, before establishing the optimization node, the method further includes: defining initial values ​​of global variables in the model developer; creating parameter sub-nodes, wherein the parameter sub-nodes include battery capacity, state of charge, and temperature.

[0014] Optionally, when the difference between the simulated change curve and the measured change curve reaches a preset difference, determining the corresponding multiple overpotentials includes: setting an operation function in the model developer; calculating the operation function in the model developer to determine the simulated change curves of multiple overpotentials; and determining the corresponding overpotentials through the simulated change curves of the overpotentials.

[0015] Optionally, determining the multiple overpotential simulation change curves by operating the operation function in the model developer includes: obtaining a group diagram of voltage loss and load by operating the operation function in the model developer; determining the multiple overpotential simulation change curves (such as Figure 13-2 shown).

[0016] According to another aspect of an embodiment of the present invention, a battery internal resistance determination device is also provided, comprising: an acquisition module, used to acquire the measured potential and applied current of the battery, and the battery equilibrium potential of the battery under multiple charge states during the process of measuring the DC internal resistance; an equation module, used to obtain a simulated change curve of the battery potential through multiple overpotentials, the battery equilibrium potential and the potential equation of the battery potential; an optimization module, used to bring the simulated change curve close to the measured change curve by changing the values ​​of the multiple overpotentials, wherein the measured change curve is a change curve of the measured potential versus time; a first determination module, used to determine the corresponding multiple overpotentials when the difference between the simulated change curve and the measured change curve reaches a preset difference; and a second determination module, used to determine the multiple impedances of the battery based on the determined multiple overpotentials and the applied current.

[0017] According to another aspect of an embodiment of the present invention, a computer storage medium is further provided, wherein the computer storage medium includes a stored program, wherein when the program is executed, the device where the computer storage medium is located is controlled to execute any one of the above-mentioned methods for determining the internal resistance of a battery.

[0018] According to another aspect of an embodiment of the present invention, a processor is further provided, wherein the processor is used to run a program, wherein the program executes any one of the above-mentioned methods for determining the internal resistance of a battery when running.

[0019] In an embodiment of the present invention, in the process of measuring the DC internal resistance, the measured potential and applied current of the battery, as well as the battery equilibrium potential of the battery under multiple charge states are obtained; a simulated change curve of the battery potential is obtained through a potential equation of multiple overpotentials, battery equilibrium potential and battery potential; the simulated change curve is brought close to the measured change curve by changing the values ​​of multiple overpotentials, wherein the measured change curve is the change curve potential of the measured potential versus time; when the difference between the simulated change curve and the measured potential change curve reaches a preset difference, the corresponding multiple overpotentials are determined; according to the determined multiple overpotentials and applied currents, a method of determining multiple impedances of the battery is used, and the simulated change curve is brought close to the measured change curve potential by optimizing the values ​​of multiple overpotentials to fit the specific battery cell properties, so as to predict the DC internal resistance of other individual batteries of the same system battery, and a predicted mainstream internal resistance close to the measured value and its decomposition are obtained, thereby solving the technical problems of low detection accuracy and incomplete detection in the method of detecting batteries by technical parameters in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0021] Figure 1 is a flow chart of a method for determining battery internal resistance according to an embodiment of the present invention;

[0022] Figure 2 is a schematic diagram of a main interface of a model builder according to an embodiment of the present invention;

[0023] Figure 3 is a schematic diagram of a global parameter definition interface according to an embodiment of the present invention;

[0024] Figure 4 is a schematic diagram of a lumped battery node interface according to an embodiment of the present invention;

[0025] Figure 5 is a schematic diagram of a battery balancing potential subnode interface according to an embodiment of the present invention;

[0026] Figure 6 is a schematic diagram of a voltage loss subnode interface according to an embodiment of the present invention;

[0027] Figure 7 is a schematic diagram of an optimized node interface according to an embodiment of the present invention;

[0028] Figure 8 is a schematic diagram of a study 1 interface according to an embodiment of the present invention;

[0029] Fig. 9 is a schematic diagram of a Study 2 interface according to an embodiment of the present invention;

[0030] Fig.10 is a schematic diagram of a battery voltage interface of a calculation result according to an embodiment of the present invention;

[0031] Fig.11 is a schematic diagram of a model interface of a calculation result according to an embodiment of the present invention;

[0032] Figure 12-1 is a schematic diagram of a battery simulation potential and a battery measured change curve of a first calculation result according to an embodiment of the present invention;

[0033] Figure 12-2 is a schematic diagram of ohm, concentration difference, and activation overpotential of a first calculation result according to an embodiment of the present invention;

[0034] Figure 13-1 is a schematic diagram of a battery simulation potential and a battery measured change curve of a second calculation result according to an embodiment of the present invention;

[0035] Figure 13-2 is a schematic diagram of ohm, concentration difference, and activation overpotential of a second calculation result according to an embodiment of the present invention;

[0036] Figure 14-1 is a schematic diagram of a battery simulation potential and a battery measured change curve of a third calculation result according to an embodiment of the present invention;

[0037] Figure 14-2 is a schematic diagram of ohm, concentration difference, and activation overpotential of a third calculation result according to an embodiment of the present invention;

[0038] Fig.15 is a schematic diagram comparing simulated DCR and measured DCR results according to an embodiment of the present invention;

[0039] Fig.16 is a schematic diagram of a battery internal resistance determination device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0041] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0042] According to an embodiment of the present invention, a method embodiment of a method for determining the internal resistance of a battery is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0043] Figure 1 is a flow chart of a method for determining battery internal resistance according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0044] Step S102, in the process of measuring the DC internal resistance, obtaining the measured potential and applied current of the battery, and the battery equilibrium potential of the battery at multiple charge states;

[0045] Step S104, obtaining a simulated change curve of the battery potential through a plurality of overpotentials, battery equilibrium potentials and potential equations of the battery potential;

[0046] Step S106, changing the values ​​of multiple overpotentials to bring the simulated change curve close to the measured change curve, wherein the measured change curve is a change curve of the measured potential versus time, and the measured potential;

[0047] Step S108, when the difference between the simulated change curve and the measured potential change curve reaches a preset difference, determining a corresponding plurality of overpotentials;

[0048] Step S110, determining multiple impedances of the battery according to the determined multiple overpotentials and applied currents.

[0049] Through the above steps, the measured potential and applied current of the battery, as well as the battery equilibrium potential of the battery under multiple charge states are obtained during the process of measuring the DC internal resistance; a simulated change curve of the battery potential is obtained through potential equations of multiple overpotentials, battery equilibrium potential and battery potential; the simulated change curve is brought close to the measured change curve by changing the values ​​of multiple overpotentials, wherein the measured change curve is the measured potential of the change curve of the measured potential and time; when the difference between the simulated change curve and the measured potential change curve reaches a preset difference, the corresponding multiple overpotentials are determined; according to the determined multiple overpotentials and applied currents, the multiple impedances of the battery are determined, and the simulated change curve is brought close to the measured change curve by optimizing the values ​​of multiple overpotentials to fit the specific battery cell properties, so as to predict the DC internal resistance of other individual batteries of the same system battery, and the predicted mainstream internal resistance close to the measured value and its decomposition are obtained, thereby solving the technical problems of low detection accuracy and incomplete detection in the related art of detecting batteries by technical parameters.

[0050] There are many ways to measure the DC internal resistance, for example, the DC method and the AC method. In this embodiment, the DC method is used to measure the DC internal resistance of the battery. Specifically, the DC method is to apply a constant current pulse of a specific rate and time to the battery in a certain state of charge (State of Charge, SOC), record the voltage before charge and discharge (V1) and the charge and discharge termination voltage (V2), and divide the difference between the two voltages by the constant current (V1-V2) / I to obtain the corresponding charge and discharge DC internal resistance. The above-mentioned measured potential can be the battery potential actually measured in the process of measuring the DC internal resistance by the DC method. The above-mentioned applied current can be the above-mentioned cross-current I.

[0051] The battery balance potentials of the battery under multiple states of charge may be battery balance potentials when the battery is under different states of charge SOC.

[0052] The simulated change curve of the battery potential is obtained through the potential equations of multiple overpotentials, battery equilibrium potentials and battery potentials. The above potential equations are used to express the equation relationship between the above multiple overpotentials, battery equilibrium potentials and battery potentials. Specifically, the battery potential is equal to the superposition of the battery equilibrium potential of the battery at a certain time and a certain state of charge and multiple overpotentials. The simulated change curve of the battery potential can be determined through the above equations and the battery equilibrium potential of the battery at multiple times and multiple states of charge.

[0053] By changing the values ​​of multiple overpotentials in the above potential equation, the simulated change curve is brought close to the measured change curve. Theoretically, the simulated change curve can only be infinitely close to the measured change curve. In this embodiment, when the difference between the simulated change curve and the measured change curve reaches a preset difference, it is determined that the difference between the simulated change curve and the measured potential meets the requirement, and it is determined that the difference between the multiple overpotentials corresponding to the simulated change curve at this time and the overpotential of the real battery meets the requirement.

[0054] The above-mentioned preset difference value can be a tolerance. When the simulated change curve is close to the measured change curve, an algorithm is used to bring the simulated change curve close to the measured change curve. The algorithm is correspondingly provided with a preset value. For example, when the above-mentioned algorithm is the least squares method, the above-mentioned preset difference value is the tolerance. When the tolerance of the simulated change curve and the measured change curve meets the preset tolerance, it is determined that the proximity of the simulated change curve and the measured change curve meets the requirements, and the algorithm is terminated to obtain the above-mentioned multiple overpotentials.

[0055] Therefore, the above multiple overpotentials are used as the real overpotentials of the battery, and multiple impedances of the battery are determined according to the applied current. The proportion of a certain impedance in a certain time period can also be obtained.

[0056] Optionally, the multiple overpotentials include ohmic overpotential, electrochemical reaction overpotential and concentration overpotential; obtaining a simulated change curve of the battery potential through multiple overpotentials, battery equilibrium potential and potential equation of the battery potential includes: obtaining the potential equation, the potential equation is Ecell=Eocv(SOCave,T)+ηIR+ηact+ηconc, wherein Ecell is the battery potential, Eocv(SOCave,T) is a function of the battery equilibrium potential with respect to the state of charge and temperature, ηIR is the ohmic overpotential, ηact is the electrochemical reaction overpotential, and ηconc is the concentration overpotential; setting initial empirical values ​​for the ohmic overpotential, electrochemical reaction overpotential and concentration overpotential, and obtaining a simulated change curve of the battery potential through multiple battery equilibrium potentials.

[0057] Through modeling and mathematical optimization methods (such as least squares method), mainly by selecting the η of the battery cell IR , η act , η conc The three parameters are optimized as variables to obtain η close to the true value. IR , η act , η conc That is, the ohmic overpotential, electrochemical reaction overpotential and concentration difference overpotential values ​​are obtained. In addition, since the present embodiment adopts the direct current method, after obtaining the above overpotential values, the ohmic impedance, charge transfer impedance and concentration difference impedance can be respectively calculated according to the applied current.

[0058] Get η close to the true value IR , η act , η conc Modeling process: During the modeling process, the equation E in the software is called cell =E ocv (SOC ave ,T)+η IR +η act +η conc ; and use the least squares method to make the E of the equation called in the modeling cell The value is close to the E_cell_exp value of the imported measured data (imported after actual measurement).

[0059] Make E cell The process of approaching the true E_cell_exp value: Since the equation E cell =E ocv (SOC ave ,T)+η IR +η act +η conc Among them, only E ocv (SOC ave ,T) is a variable, and η IR +η act+η conc are all initial preset experience values, so the system can obtain ocv (SOC ave ,T) changes automatically obtained E cell Curve. E ocv (SOC ave , T) is the battery equilibrium potential of the battery cell at a certain SOC; by importing the measured data, that is, the measured battery equilibrium potential of the battery cell at different SOCs into the system software, the variable E can be obtained ocv (SOC ave ,T) value, thus obtaining E cell curve.

[0060] Then, after obtaining the above curve, the least square method is used to make E cell The curve is close to the real E_cell_exp curve. In this process, η IR , η act , η con The changes of the three target variables make E cell The curve is close to the true E_cell_exp curve. Therefore, after the least squares calculation, we get E cell While the curve is close to the true value E_cell_exp, η IR , η act , η con The three target variables are also gradually adjusted to values ​​close to the real ones, thus achieving the goal of this modeling.

[0061] In addition, from the test results of the present application, not only can the specific values ​​of the three impedances of DCR, including ohmic impedance, charge transfer impedance, and concentration impedance, be obtained, but also the proportion of a certain impedance in a certain time period can be obtained; in different time zones, the three impedances will exist, but the proportions of the three impedances in each time zone are different, so the test results of the present application can be more accurate.

[0062] Optionally, before being applied to a model developer and obtaining a simulated change curve of the battery potential through potential equations of multiple overpotentials, battery equilibrium potentials and battery voltages, the method further includes: establishing a lumped battery physical field node corresponding to the battery in the model developer, wherein the lumped battery physical field node includes a battery equilibrium potential sub-node and a voltage loss sub-node; obtaining multiple overpotentials, battery equilibrium potentials and potential equations of the battery potential through the lumped battery physical field node; generating a function of the battery equilibrium potential to the state of charge and temperature according to the battery equilibrium potential under multiple states of charge and the corresponding temperatures through the battery balance positioning sub-node; and determining multiple overpotentials through the voltage loss sub-node.

[0063] The above-mentioned model developer may be a COMSOL Multiphysics software platform, and establishing a lumped battery physical field node corresponding to the battery in the model developer may be establishing a zero-dimensional lithium-ion lumped battery node;

[0064] In the Lumped Battery Node Settings column, adjust the operating mode to constant current and apply current I app =I_cell_exp / 1000, I_cell_exp is the measured applied current;

[0065] Set the battery pack capacity Q_cell and initial state of charge;

[0066] The lumped battery physical field node includes a battery balance potential subnode and a voltage loss subnode. In the battery balance potential subnode, the battery balance potential (open circuit voltage) corresponding to different SOCs of the measured battery is imported and the reference temperature is set.

[0067] In the Voltage Loss subnode, set the variable Ohmic Overpotential η IR =eta_IR_1C; set variable activation overpotential J0 = J0; diffusion time constant τ = tau.

[0068] Optionally, bringing the simulated change curve close to the measured change curve by changing the values ​​of multiple overpotentials includes: establishing an optimization node, and changing the values ​​of multiple overpotentials through the optimization node according to a least squares method to bring the simulated change curve close to the measured change curve.

[0069] Establish an optimization node with measured voltage as the target; after adding the Optimize Physics node, add a Global Least Squares subnode, with the parameter type as time, the time unit as seconds, the time column as T(s), and the data column used as V(V). Specifically, after adding the Optimize Physics node, under Mathematical Options, Optimize node, add a Global Least Squares Target subnode, with the parameter type as time, the time unit as seconds, the time column as T(s), and the data column used as V(V), the model expression as lb.E_cell, and the weight as 1.

[0070] Optionally, before establishing the optimization node, the method further includes: defining initial values ​​of global variables in the model developer; and creating parameter sub-nodes, wherein the parameter sub-nodes include battery capacity, state of charge, and temperature.

[0071] Import global parameters and measured data; for example, establish the following parameters: eta_IR_1C = 10 [mV]; invJ0 = 1; tau = 1000 [s]; J0 = 1; Q_cell0 = 44 [A*h]; SOC_0 = 1, T = 298.15 [K];

[0072] Import the battery balance potential of the cells at different states of charge at the reference temperature to the Battery Balance Potential subnode; create a load cycle data table in the Result-Table subnode, and use Excel or text to separate the measured time, voltage, and current data into three columns before importing;

[0073] Create an interpolation function under the definition node, the data source is the load cycle data table, and create functions E_cell_exp and I_cell_exp; among them, E_cell_exp is the measured voltage and I_cell_exp is the measured current.

[0074] Create parameter subnodes in the global definition, and define the initial values ​​of eta_IR_1C, J0, tau, the cell capacity Q_cell0, the initial state of charge SOC_0, and the temperature T. The cell capacity, initial state of charge, and temperature can be determined as needed.

[0075] Create a load cycle data table in the Results-Table subnode. You can use Excel or text notepad to separate the measured time, voltage, and current data into three columns and then import them.

[0076] Create an interpolation function under the definition node, with the data source being the load cycle data table, and assign values ​​to variables E_cell_exp and I_cell_exp;

[0077] Among them, E_cell_exp is the measured voltage, I_cell_exp is the measured current;

[0078] Furthermore, create probes under the definition node to monitor eta_IR_1C, invJ0, tau, and comp 1.ib.E_cell respectively (the observation results can be observed in real time, and other probes can be created as needed);

[0079] Optionally, when the difference between the simulated change curve and the measured change curve reaches a preset difference, determining the corresponding multiple overpotentials includes: setting an operation function in the model developer; calculating the operation function in the model developer to determine the simulated change curves of the multiple overpotentials; and determining the corresponding overpotentials through the simulated change curves of the overpotentials.

[0080] The above operation function may be Time Dependent or Levenberg-Marquardt.

[0081] Specifically, in the above model builder, set the Study 1 subnode to execute the above calculation function TimeDependent, and set the step size to range(0,1,4000); the capacity is user controlled, and the relative tolerance is 0.001; the physics interface is checked for lumped battery (lb), and optimization (opt) is not selected;

[0082] Alternatively, add Study 2 - Parameter Estimation in the above model builder and add an Optimization subnode; select the above operation function Levenberg-Marquardt as the optimization solver method, set the tolerance to 0.001, the study step to transient, and the maximum number of model calculations to 1000; in the control variable and parameter selection settings, select eta_IR_1C, invJ0, tau, and set the approximate range; eta_IR_1C is 0.01, invJ0 is 1, and tau is 1000;

[0083] In the optimization subnode, check the option to plot the battery voltage and keep the target value in the table. The output table is Objective Probe Table 3. Then, input the corresponding result data, generate a change curve based on the result data, and determine the corresponding overpotential based on the change curve.

[0084] Optionally, determining multiple simulated change curves of overpotentials by operating an operation function in a model developer includes: obtaining a group diagram of voltage loss and load by operating an operation function in a model developer; and determining multiple simulated change curves of overpotentials based on the group diagram.

[0085] Under the Results node, add a 1D plot group line graph, name it Voltage Loss and Load, set the y-axis label to Overvoltage (V), check Dual y-axes, check Show legend, and set the position to the lower right corner; the x-axis expression is t, the unit is s, where t is the test time;

[0086] Create a global graph under the line graph, the data set comes from the parent item, and the y-axis expressions are lb.eta_ir, lb.eta_conc, and lb.eta_act respectively;

[0087] Among them, lb.eta_ir is the ohmic overpotential, lb.eta_conc is the concentration overpotential, and lb.eta_act is the activation overpotential, which is the electrochemical reaction overpotential.

[0088] It should be noted that the embodiment of the present application also provides an optional implementation, which is described in detail below.

[0089] This embodiment provides a simulation method that has high accuracy, good operability, low requirements for input data, low cost, and can predict and decompose the DCR of a power cell.

[0090] The main principles of this implementation are:

[0091] Based on the COMSOL Multiphysics software platform, through modeling and mathematical optimization methods (such as the least squares method), mainly by selecting the three parameters of ηIR, ηact, and ηconc of the battery cell as variables for optimization, ηIR, ηact, and ηconc close to the true value can be obtained; that is, the ohmic overpotential, electrochemical reaction overpotential, and concentration difference overpotential values ​​are obtained. In addition, since the present invention is a direct current method, after obtaining the above-mentioned overpotential values, the ohmic impedance, charge transfer impedance, and concentration difference impedance can be respectively calculated according to the applied current.

[0092] Get η close to the true value IR , η act , η conc Modeling process:

[0093] During the modeling process, the software equation E is called cell =E ocv (SOC ave ,T)+η IR +η act +η conc ; and use the least squares method to make the E of the equation called in the modeling cell The value is close to the E_cell_exp value of the imported measured data (imported after actual measurement).

[0094] Make E cell The process of getting the value close to the true E_cell_exp value:

[0095] Since in the called equation E cell =E ocv (SOC ave ,T)+η IR +η act +η conc Among them, only E ocv (SOC ave ,T) is a variable, and η IR +η act +η conc are all initial preset experience values, so the system can obtain ocv (SOC ave ,T) changes automatically obtained E cell Curve. E ocv (SOC ave, T) is the battery equilibrium potential of the battery cell at a certain SOC; by importing the measured data, that is, the measured battery equilibrium potential of the battery cell at different SOCs into the system software, the variable E can be obtained ocv (SOC ave ,T) value, thus obtaining E cell curve.

[0096] Then, after obtaining the above curve, the least square method is used to make E cell The curve is close to the real E_cell_exp curve. In this process, η IR , η act , η con The changes of the three target variables make E cell The curve is close to the true E_cell_exp curve. Therefore, after the least squares calculation, we get E cell While the curve is close to the true value E_cell_exp, η IR , η act , η con The three target variables are also gradually adjusted to values ​​close to the real ones, thus achieving the goal of this modeling.

[0097] In addition, from the test results of this embodiment, not only the specific values ​​of the three ohmic impedances of DCR, charge transfer impedance, and concentration impedance can be obtained, but also the proportion of a certain impedance in a certain time period can be obtained; in different time zones, the three impedances will exist, but the proportions of the three impedances in each time zone are different, so the test results of this application can be more accurate.

[0098] Specific steps:

[0099] Based on the COMSOL Multiphysics software platform, that is, the model builder, such as Figure 2 As shown, Figure 2 Schematic diagram of the main interface of the model builder according to an embodiment of the present invention, the specific steps are as follows (the order of step 1 and step 2 can be changed):

[0100] Step 1: Modeling; Establishing the physical field node of lithium-ion lumped battery; including setting the subnode of battery equilibrium potential and the subnode of voltage loss (modeling);

[0101] Step 2: Definition; import global parameters and measured current and voltage data, such as Figure 3 As shown, Figure 3 is a schematic diagram of a global parameter definition interface according to an embodiment of the present invention;

[0102] Step 3: Establish an optimization node, and the optimization target is the measured voltage;

[0103] Step 4: Establish study node 1 to describe the state of the battery during the charging process, set the study step size, and control variables and parameters including ohmic overpotential, diffusion coefficient, and current exchange constant; establish study node 2 and add global least squares objective settings (which can be other mathematical calculation methods);

[0104] Step 5: Calculate the optimized parameters and overpotential to obtain DCR.

[0105] Main content:

[0106] Step 1 is as follows: Create a zero-dimensional lithium-ion lumped battery node, such as Figure 4 As shown, Figure 4 is a schematic diagram of a lumped battery node interface according to an embodiment of the present invention

[0107] In the Lumped Battery Node Settings column, adjust the operating mode to constant current and apply current I app =I_cell_exp / 1000, I_cell_exp is the measured applied current;

[0108] Set the battery pack capacity Q_cell and initial state of charge;

[0109] Figure 5 is a schematic diagram of a battery balance potential subnode interface according to an embodiment of the present invention, such as Figure 5 As shown, in the battery balance potential subnode, the open circuit voltage corresponding to different SOCs of the measured battery is imported, that is, the battery balance potential, and the reference temperature is set;

[0110] Figure 6 is a schematic diagram of a voltage loss subnode interface according to an embodiment of the present invention, such as Figure 6 As shown, in the Voltage Loss subnode, set the variable Ohmic Overpotential η IR =eta_IR_1C; set variable activation overpotential J0 = J0; diffusion time constant τ = tau;

[0111] Step 2 is specifically as follows: import global parameters and measured data;

[0112] For example, the following parameters are established: eta_IR_1C=10[mV]; invJ0=1; tau=1000[s]; J0=1; Q_cell0=44[A*h]; SOC_0=1; T=298.15[K].

[0113] Import the open circuit voltage of the battery cell at different states of charge at the reference temperature to the Battery Balance Potential subnode; create a load cycle data table in the Result-Table subnode, and use Excel or text to separate the measured time, voltage, and current data into three columns before importing;

[0114] Create an interpolation function under the definition node, the data source is the load cycle data table, and create functions E_cell_exp and I_cell_exp; where E_cell_exp is the measured potential and I_cell_exp is the measured current;

[0115] Step 3 is as follows: establish an optimization node with the measured potential as the target, such as Figure 7 As shown, Figure 7 is a schematic diagram of an optimized node interface according to an embodiment of the present invention;

[0116] After adding the Optimize Physics node, add a Global Least Squares subnode, set the parameter type to Time, the time unit to seconds, the time column to T(s), and the data column to use to V(V);

[0117] Step 4 is as follows:

[0118] Figure 8 is a schematic diagram of the interface of Study 1 according to an embodiment of the present invention, such as Figure 8 As shown, set the study 1 subnode step 1: Time Dependent, set the step size to range(0,1,4000); the capacity is user controlled, and the relative tolerance is 0.001; check the lumped battery (lb) for the physical field interface, and do not select optimization (opt);

[0119] Fig. 9 is a schematic diagram of the interface of Study 2 according to an embodiment of the present invention, such as Fig. 9 As described above, add Study 2 - Parameter Estimation and add the Optimization subnode;

[0120] The optimization solver method is Levenberg-Marquardt, the tolerance is 0.001, the study step is transient, and the maximum number of model calculations is 1000;

[0121] In the control variable and parameter selection settings, select eta_IR_1C, invJ0, tau, and set the approximate range; eta_IR_1C is 0.01, invJ0 is 1, and tau is 1000;

[0122] In the optimization subnode, under Output during solution, check Plot battery voltage, check Keep objective values ​​in table, and the output table is Objective Probe Table 3.

[0123] Step 5 is as follows:

[0124] Fig.10 is a schematic diagram of a battery voltage interface of a calculation result according to an embodiment of the present invention, Fig.11 is a schematic diagram of a model interface of a calculation result according to an embodiment of the present invention, such as Fig.10 and Fig.11 As shown, click Calculate, under the Result node, add a 1D plot group line graph, name it Voltage Loss and Load, set the y-axis label to Overvoltage (V), check Dual y-axes, check Show legend, and set the position to the lower right corner; the x-axis expression is t, the unit is s, where t is the test time;

[0125] Create a global graph under the line graph, the data set comes from the parent item, and the y-axis expressions are lb.eta_ir, lb.eta_conc, and lb.eta_act respectively;

[0126] Among them, lb.eta_ir is the ohmic overpotential, lb.eta_conc is the concentration overpotential, and lb.eta_act is the activation overpotential;

[0127] More specifically, the step 1 is:

[0128] 1. Perform the following operations in the Lumped Battery Node Settings column:

[0129] Select to import the following calculation formula (Specific operation: Select "Research Control" under this node, the system software will automatically import it by default, and the specific setting parameters and interface are as follows Figure 4 shown):

[0130] E cell =E ocv (SOC ave ,T)+η IR +η act ;

[0131] I 1C =Q_cell0 / 3600[s];

[0132] I cell =I app ;

[0133] Further, the applied current, battery pack capacity and initial state of charge SOC are selected.

[0134] (Specific operation: select "Transient", adjust the "Working mode" to "Constant current", and select "I app =I_cell_exp / 1000", where "I_cell_exp" is set to the actual applied current, and the unit must be ampere;

[0135] Set the "battery pack capacity" and "initial state of charge SOC"; the specific setting parameters and interface are as follows Figure 4 shown);

[0136] 2. Perform the following operations in the battery balance potential subnode (specific setting parameters and interface are as follows Figure 5 shown):

[0137] Call out the control equation (Specific operation: Select "Study 1 - Load Curve Simulation", Time Dependent; Call out the control equation as follows Figure 5 shown):

[0138] Set the reference temperature "T"; select "Study 1 - Load Curve Simulation", "Time Dependent";

[0139] Among them, E OCV,ref is the standard open circuit voltage, T is the system temperature, T ref is room temperature;

[0140] 3. Perform the following operations at the Voltage Loss subnode:

[0141] Call the following formula:

[0142] E cell =E ocv (SOC ave ,T)+η IR +η act +η conc ;

[0143] lb.eta_ir = ηIR;

[0144] lb.eta_conc = ηconc;

[0145] lb.eta_act = ηact;

[0146] η IR =η IR，1C *I cell / I 1C ;

[0147] η act =2RT / F*asinh*I cell / 2J0I 1C ;

[0148]

[0149] ▽SOC| x＝1 =τI cell / 3Q cell,0 ;

[0150] ▽SOC| x＝0 =0;

[0151] SOC|t＝0 =SOC cell,0 ;

[0152]

[0153] η conc =E OCV (SOC| x＝1 ,T)-E OCV (SOC ave ,T);

[0154] Among them, E cell is the battery potential, E ocv (SOC ave ,T) is the function of potential equilibrium potential with SOC and temperature, η IR is the ohmic overpotential, η act is the electrochemical reaction overpotential, η conc is the concentration overpotential; lb.eta_ir is the parameter that the software uses by default to refer to the ohmic overpotential, lb.eta_conc is the parameter that the software uses by default to refer to the concentration overpotential, lb.eta_act is the parameter that the software uses by default to refer to the concentration overpotential, Q cell,0 is the cell capacity, SOC cell,0 is the initial SOC, SOC| x＝1 is the outermost SOC of the pole piece, SOC| x＝0 The innermost SOC of the electrode, I cell is the applied current, I 1C is the current at 1C rate, R is the thermodynamic constant, and F is the Faraday constant;

[0155] Specific operations such as Figure 6 As shown: Set the variable Ohmic overpotential η IR =eta_IR_1C”; check “Include activation overpotential” and set “Variable activation overpotential J0 = J0”; and set the diffusion time constant “τ = tau”:

[0156] Furthermore, check the box that includes the concentration overpotential type as "Particle Diffusion" and the ion type as "Sphere";

[0157] Furthermore, in the Voltage Loss subnode, the ion discretization is selected as "cube root sequence", the number of units is "10", and the unit order is "2nd order".

[0158] The specific operations of step 2 are:

[0159] Create a parameter subnode in the global definition, and define the initial values ​​of eta_IR_1C, J0, tau, the cell capacity Q_cell0, the initial state of charge SOC_0, and the temperature T. The cell capacity, initial state of charge, and temperature can be determined according to the situation, such as Figure 3 As shown;

[0160] Create a load cycle data table in the Results-Table subnode. You can use Excel or text notepad to separate the measured time, voltage, and current data into three columns and then import them.

[0161] Create an interpolation function under the definition node, with the data source being the load cycle data table, and assign values ​​to variables E_cell_exp and I_cell_exp;

[0162] Among them, E_cell_exp is the measured potential, I_cell_exp is the measured current;

[0163] Furthermore, create probes under the definition node to monitor eta_IR_1C, invJ0, tau, and comp 1.ib.E_cell respectively (the observation results can be observed in real time, and other probes can be created as needed);

[0164] The step 3 is specifically as follows ( Figure 6 shown):

[0165] Specifically, after adding the Optimize Physics node, under Mathematical Options, in the Optimize node, add a Global Least Squares Objective subnode, with the parameter type set to Time, the time unit set to seconds, the time column set to T(s), the data column set to V(V), the model expression set to lb.E_cell, and the weight set to 1;

[0166] The step 4 is specifically as follows:

[0167] Set up Transient Study 1, name it Load Curve, select Manual for Time / Parameter List, and set the Step Size to Seconds; Time Step Range (0,1,4000); Capacity to User Control, Relative Tolerance 0.001; Check Lumped Battery (lb) for the Physics Interface, and do not select Optimization (opt);

[0168] Add a Transient Study 2, name it Parameter Estimation, and add an Optimization subnode. In the Optimization Solver Method, select an appropriate solution method (choose one that matches the least squares method), set the Tolerance to 0.001, the Study Step to Transient, and set the Maximum Number of Model Calculations to 1000 (the more the better, choose according to actual needs).

[0169] In the control variable and parameter selection settings, select eta_IR_1C, invJ0, tau, and set the scaling range;

[0170] Set the output plot group properties in the optimization node;

[0171] The step 5 is specifically as follows:

[0172] Click Calculate, under Results, add a 1D Plot Group Line Graph, name it Voltage Loss and Load, set the y-axis label to Overvoltage (V), check Dual y-axes, check Show legend, and set the position to lower right corner;

[0173] Create a global graph under the line graph node. The data set comes from the parent item. The y-axis expressions are lb.eta_ir, lb.eta_conc, and lb.eta_act respectively.

[0174] Among them, lb.eta_ir is the ohmic overpotential, lb.eta_conc is the concentration overpotential, and lb.eta_act is the activation overpotential.

[0175] This embodiment uses COMSOL Multiphysics software to establish a zero-dimensional lumped lithium-ion battery model. By solving the partial differential equations defined by electrochemistry, kinetics, and thermodynamics, the DCR value of the power cell is simulated, the parameters are optimized, and its components are decomposed. By introducing optimization nodes to optimize the three parameters (η IR , η act and η conc ) The least squares optimization solution was performed with the measured value as the target, so that the model fits the specific cell properties in order to predict the DCR of other individual cells in the same system battery. The DCR of the power cell under different SOC states was discussed by changing the model parameters, and compared with the actual value, and the predicted value DCR and its decomposition close to the measured value were obtained.

[0176] The present invention is described in detail below in conjunction with specific embodiments.

[0177] The research object is a 44Ah lithium-ion soft pack battery. Its composition is a porous carbon negative electrode, Ni 0.5 Co 0.2 Mn 0.3O2 ternary material positive electrode, ceramic polyethylene film for diaphragm, lithium hexafluorophosphate carbonate-based electrolyte (lithium hexafluorophosphate, ethylene carbonate EC, dimethyl carbonate DMC, ethyl methyl carbonate EMC). The main instruments and equipment used in the DCR test include a computer, a Xinwei battery tester (CT-4004-5V300A-NTFA) for charging and discharging the battery, a high-speed data acquisition instrument to detect the battery voltage, an Autolab PGSTAT302N electrochemical workstation, and other equipment are commonly used accessories. The test interval is 5% SOC, the range is 0-100%, and a 10-second 6C current pulse is applied to obtain the charge and discharge DCR.

[0178] OCV test method:

[0179] Temperature 25℃, SOC range 0-100%, interval 5%; discharge rate 1C, standby time between test points 2 hours; voltage upper limit 4.2V, lower limit 2.7V.

[0180] DCR test method:

[0181] Temperature 25℃, SOC range 0-100%, interval 10%; discharge rate 6C, discharge time 10s, charge rate 6C, charge time 10s, test data recording 0.1s; voltage upper limit 4.2V, lower limit 2.7V.

[0182] Actual measured voltage:

[0183] The first implementation method:

[0184] Step 1 is specifically as follows: establish a zero-dimensional lithium-ion lumped battery node;

[0185] In the Lumped Battery Node Settings column, adjust the operating mode to constant current and apply current I app =I_cell_exp / 1000, I_cell_exp is the measured applied current;

[0186] Set the battery pack capacity Q_cell0 to 44Ah and the initial state of charge SOC100%;

[0187] In the battery balance potential subnode, import the open circuit voltage corresponding to different SOC of the measured battery and the reference temperature T = 298.15K (as long as they are consistent);

[0188] In the Voltage Loss subnode, set the variable Ohmic Overpotential η IR =eta_IR_1C; set variable activation overpotential J0 = J0; diffusion time constant τ = tau;

[0189] Step 2 is specifically as follows: import global parameters and measured data;

[0190] The following parameters were established: eta_IR_1C = 10 [mV]; invJ0 = 1; tau = 1000 [s]; J0 = 1; Q_cell0 = 44 [A*h]; SOC_0 = 1; T = 298.15 [K];

[0191] Import the open circuit voltage of the battery cell at different states of charge at the reference temperature to the Battery Balance Potential subnode; create a load cycle data table in the Result-Table subnode, and use Excel or text to separate the measured time, voltage, and current data into three columns before importing;

[0192] Create an interpolation function under the definition node, the data source is the load cycle data table, and create functions E_cell_exp and I_cell_exp; where E_cell_exp is the measured potential and I_cell_exp is the measured current;

[0193] Step 3 specifically includes: establishing an optimization node with the measured potential as the target;

[0194] After adding the Optimize physics node, add a Global Least Squares subnode, with the data source as the result table, the result table as the load cycle data, the parameter type as time, the time unit as seconds, the time column as T(s), and the data column used as V(V);

[0195] Step 4 is as follows:

[0196] Set up Study 1 subnode Step 1: Time Dependent, set the step size to range(0,1,4000); set the capacity to user controlled, with a relative tolerance of 0.001; select Lumped Battery (lb) for the physics interface, and do not select Optimization (opt);

[0197] Add Study 2 - Parameter Estimation and add Optimization subnode;

[0198] The optimization solver method is Levenberg-Marquardt, the tolerance is 0.001, the study step is transient, and the maximum number of model calculations is 1000;

[0199] In the control variable and parameter selection settings, select eta_IR_1C, invJ0, tau, and set the approximate range; eta_IR_1C is 0.01, invJ0 is 1, and tau is 1000;

[0200] In the optimization subnode, under Output during solution, check Plot battery voltage, check Keep objective values ​​in table, and the output table is Objective Probe Table 3.

[0201] Step 5 is as follows:

[0202] Click Calculate, under the Results node, add a 1D plot group line graph, name it Voltage Loss and Load, set the y-axis label to Overvoltage (V), check Dual y-axes, check Show legend, and set the position to the lower right corner; the x-axis expression is t, the unit is s, where t is the test time;

[0203] Create a global graph under the line graph, the data set comes from the parent item, and the y-axis expressions are lb.eta_ir, lb.eta_conc, and lb.eta_act respectively;

[0204] Among them, lb.eta_ir is the ohmic overpotential, lb.eta_conc is the concentration overpotential, and lb.eta_act is the activation overpotential;

[0205] Figure 12-1 is a schematic diagram of a battery simulation potential and a battery measured potential curve according to a first calculation result of an embodiment of the present invention, Figure 12-2 is a schematic diagram of ohm, concentration difference, and activation overpotential of the first calculation result according to an embodiment of the present invention, such as Figure 12-1 and Figure 12-2 As shown, it can be seen that the battery simulation potential is compared with the battery measured potential curve, the ohm, concentration difference, and activation overpotential obtained in the first implementation method are decomposed, and the three overpotentials are multiplied by the pulse current size 264A to obtain R ohm , R ct , R diff Table 1 shows the DCR decomposition results obtained in the first implementation method.

[0206] Table 1 The first DCR decomposition results

[0207]

[0208] Second implementation method

[0209] Step 1 is specifically as follows: establish a zero-dimensional lithium-ion lumped battery node;

[0210] In the Lumped Battery Node Settings column, adjust the operating mode to constant current and apply current I app =I_cell_exp / 1000, I_cell_exp is the measured applied current;

[0211] Set the battery pack capacity Q_cell0 to 44Ah and the initial state of charge SOC50%;

[0212] In the battery balance potential subnode, import the open circuit voltage corresponding to different SOC of the measured battery, and the reference temperature T = 298.15K;

[0213] In the Voltage Loss subnode, set the variable Ohmic Overpotential η IR =eta_IR_1C; set variable activation overpotential J0 = J0; diffusion time constant τ = tau;

[0214] Step 2 is specifically as follows: import global parameters and measured data;

[0215] The following parameters were established: eta_IR_1C = 10 [mV]; invJ0 = 1; tau = 1000 [s]; J0 = 1; Q_cell0 = 44 [A*h]; SOC_0 = 0.5; T = 298.15 [K];

[0216] Import the open circuit voltage of the battery cell at different states of charge at the reference temperature to the Battery Balance Potential subnode; create a load cycle data table in the Result-Table subnode, and use Excel or text to separate the measured time, voltage, and current data into three columns before importing;

[0217] Create an interpolation function under the definition node, the data source is the load cycle data table, and create functions E_cell_exp and I_cell_exp; where E_cell_exp is the measured potential and I_cell_exp is the measured current;

[0218] Step 3 specifically includes: establishing an optimization node with the measured potential as the target;

[0219] After adding the Optimize physics node, add a Global Least Squares subnode, with the data source as the result table, the result table as the load cycle data, the parameter type as time, the time unit as seconds, the time column as T(s), and the data column used as V(V);

[0220] Step 4 is as follows:

[0221] Set up Study 1 subnode Step 1: Time Dependent, set the step size to range(0,1,4000); set the capacity to user controlled, with a relative tolerance of 0.001; select Lumped Battery (lb) for the physics interface, and do not select Optimization (opt);

[0222] Add Study 2 - Parameter Estimation and add Optimization subnode;

[0223] The optimization solver method is Levenberg-Marquardt, the tolerance is 0.001, the study step is transient, and the maximum number of model calculations is 1000;

[0224] In the control variable and parameter selection settings, select eta_IR_1C, invJ0, tau, and set the approximate range; eta_IR_1C is 0.01, invJ0 is 1, and tau is 1000;

[0225] In the optimization subnode, under Output during solution, check Plot battery voltage, check Keep objective values ​​in table, and the output table is Objective Probe Table 3.

[0226] Step 5 is as follows:

[0227] Click Calculate, under the Results node, add a 1D plot group line graph, name it Voltage Loss and Load, set the y-axis label to Overvoltage (V), check Dual y-axes, check Show legend, and set the position to the lower right corner; the x-axis expression is t, the unit is s, where t is the test time;

[0228] Create a global graph under the line graph, the data set comes from the parent item, and the y-axis expressions are lb.eta_ir, lb.eta_conc, and lb.eta_act respectively;

[0229] Among them, lb.eta_ir is the ohmic overpotential, lb.eta_conc is the concentration overpotential, and lb.eta_act is the activation overpotential;

[0230] Figure 13-1 is a schematic diagram of a battery simulation potential and a battery measured potential curve of a second calculation result according to an embodiment of the present invention, Figure 13-2 is a schematic diagram of ohms, concentration difference, and activation overpotential of the second calculation result according to an embodiment of the present invention, such as Figure 13-1 and Figure 13-2 As shown, it can be seen that the battery simulation potential is compared with the battery measured potential curve, the ohm, concentration difference, and activation overpotential obtained in the second implementation method are decomposed, and the three overpotentials are multiplied by the pulse current size 264A to obtain R ohm , R ct , R diff Table 2 shows the DCR decomposition results obtained in the second implementation method.

[0231] Table 2 The second DCR decomposition results

[0232]

[0233] The third implementation method

[0234] Step 1 is specifically as follows: establish a zero-dimensional lithium-ion lumped battery node;

[0235] In the Lumped Battery Node Settings column, adjust the operating mode to constant current and apply current I app=I_cell_exp / 1000, I_cell_exp is the measured applied current;

[0236] Set the battery pack capacity Q_cell0 to 44Ah and the initial state of charge SOC20%;

[0237] In the battery balance potential subnode, import the open circuit voltage corresponding to different SOC of the measured battery, and the reference temperature T = 298.15K;

[0238] In the Voltage Loss subnode, set the variable Ohmic Overpotential η IR =eta_IR_1C; set variable activation overpotential J0 = J0; diffusion time constant τ = tau;

[0239] Step 2 is specifically as follows: import global parameters and measured data;

[0240] The following parameters were established: eta_IR_1C = 10 [mV]; invJ0 = 1; tau = 1000 [s]; J0 = 1; Q_cell0 = 44 [A*h]; SOC_0 = 0.2; T = 298.15 [K];

[0241] Import the open circuit voltage of the battery cell at different states of charge at the reference temperature to the Battery Balance Potential subnode; create a load cycle data table in the Result-Table subnode, and use Excel or text to separate the measured time, voltage, and current data into three columns before importing;

[0242] Create an interpolation function under the definition node, the data source is the load cycle data table, and create functions E_cell_exp and I_cell_exp; where E_cell_exp is the measured potential and I_cell_exp is the measured current;

[0243] Step 3 specifically includes: establishing an optimization node with the measured potential as the target;

[0244] After adding the Optimize physics node, add a Global Least Squares subnode, with the data source as the result table, the result table as the load cycle data, the parameter type as time, the time unit as seconds, the time column as T(s), and the data column used as V(V);

[0245] Step 4 is as follows:

[0246] Set up Study 1 subnode Step 1: Time Dependent, set the step size to range(0,1,4000); set the capacity to user controlled, with a relative tolerance of 0.001; select Lumped Battery (lb) for the physics interface, and do not select Optimization (opt);

[0247] Add Study 2 - Parameter Estimation and add Optimization subnode;

[0248] The optimization solver method is Levenberg-Marquardt, the tolerance is 0.001, the study step is transient, and the maximum number of model calculations is 1000;

[0249] In the control variable and parameter selection settings, select eta_IR_1C, invJ0, tau, and set the approximate range; eta_IR_1C is 0.01, invJ0 is 1, and tau is 1000;

[0250] In the optimization subnode, under Output during solution, check Plot battery voltage, check Keep objective values ​​in table, and the output table is Objective Probe Table 3.

[0251] Step 5 is as follows:

[0252] Click Calculate, under the Results node, add a 1D plot group line graph, name it Voltage Loss and Load, set the y-axis label to Overvoltage (V), check Dual y-axes, check Show legend, and set the position to the lower right corner; the x-axis expression is t, the unit is s, where t is the test time;

[0253] Create a global graph under the line graph, the data set comes from the parent item, and the y-axis expressions are lb.eta_ir, lb.eta_conc, and lb.eta_act respectively;

[0254] Among them, lb.eta_ir is the ohmic overpotential, lb.eta_conc is the concentration overpotential, and lb.eta_act is the activation overpotential;

[0255] Figure 14-1 is a schematic diagram of a battery simulation potential and a battery measured potential curve of a third calculation result according to an embodiment of the present invention, Figure 14-2 is a schematic diagram of ohms, concentration difference, and activation overpotential of the third calculation result according to an embodiment of the present invention, such as Figure 14-1 and Figure 14-2 As shown, it can be seen that the battery simulation potential is compared with the battery measured potential curve, the ohm, concentration difference, and activation overpotential obtained in the third implementation method are decomposed, and the three overpotentials are multiplied by the pulse current size 264A to obtain R ohm , R ct , R diff Table 3 shows the DCR decomposition results obtained in the third implementation method. Fig.15 is a schematic diagram comparing the simulated DCR and the measured DCR results according to an embodiment of the present invention, as shown in Fig.15As shown, the simulated DCR obtained in the above three implementation modes is compared with the measured DCR results.

[0256] Table 3 Example 3 DCR decomposition results

[0257]

[0258] Fig.16 is a schematic diagram of a battery internal resistance determination device according to an embodiment of the present invention. Fig.16 As shown, according to another aspect of an embodiment of the present invention, a battery internal resistance determination device is also provided, including: an acquisition module 160, an equation module 162, an optimization module 164, a first determination module 166 and a second determination module 168. The device is described in detail below.

[0259] The acquisition module 160 is used to obtain the measured potential and applied current of the battery, as well as the battery equilibrium potential of the battery under multiple charge states during the process of measuring the DC internal resistance; the equation module 162 is connected to the above-mentioned acquisition module 160, and is used to obtain a simulated change curve of the battery potential through a plurality of overpotentials, a battery equilibrium potential and a potential equation of the battery potential; the optimization module 164 is connected to the above-mentioned equation module 162, and is used to make the simulated change curve close to the measured change curve by changing the values ​​of the plurality of overpotentials, wherein the measured change curve is a change curve of the measured potential versus time; the first determination module 166 is connected to the above-mentioned optimization module 164, and is used to determine the corresponding plurality of overpotentials when the difference between the simulated change curve and the measured change curve reaches a preset difference; the second determination module 168 is connected to the above-mentioned first determination module 166, and is used to determine the plurality of impedances of the battery according to the determined plurality of overpotentials and applied currents.

[0260] Through the above device, the acquisition module 160 is used to obtain the measured potential and applied current of the battery, as well as the battery equilibrium potential of the battery under multiple charge states during the process of measuring the DC internal resistance; the equation module 162 obtains the simulated change curve of the battery potential through the potential equations of multiple overpotentials, battery equilibrium potential and battery potential; the optimization module 164 makes the simulated change curve close to the measured change curve by changing the values ​​of multiple overpotentials, wherein the measured change curve is the change curve of the measured potential and time; the first determination module 166 determines the corresponding multiple overpotentials when the difference between the simulated change curve and the measured change curve reaches a preset difference; the second determination module 168 determines the multiple impedances of the battery according to the determined multiple overpotentials and applied currents, and optimizes the simulated change curve by changing the values ​​of multiple overpotentials to make it close to the measured change curve, so as to fit the specific battery cell properties, so as to predict the DC internal resistance of other individual batteries of the same system battery, and obtains the predicted mainstream internal resistance close to the measured value and its decomposition, thereby solving the technical problems of low detection accuracy and incomplete detection in the related art of detecting batteries by technical parameters.

[0261] According to another aspect of an embodiment of the present invention, a computer storage medium is further provided, the computer storage medium including a stored program, wherein when the program is running, the device where the computer storage medium is located is controlled to execute any one of the above-mentioned methods for determining the internal resistance of a battery.

[0262] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein when the program is run, any one of the above methods for determining the internal resistance of a battery is executed.

[0263] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0264] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0265] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0266] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0267] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0268] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0269] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining battery internal resistance, characterized in that: include: In the process of measuring the DC internal resistance, obtaining the measured potential and applied current of the battery, and the battery equilibrium potential of the battery at multiple charge states; A simulated change curve of the battery potential is obtained through a plurality of overpotentials, the battery equilibrium potential and a potential equation of the battery potential, wherein the plurality of overpotentials include an ohmic overpotential, an electrochemical reaction overpotential and a concentration difference overpotential; By changing the values ​​of the multiple overpotentials, the simulated change curve is brought close to the measured change curve, wherein the measured change curve is a change curve of the measured potential and time; When the difference between the simulated change curve and the measured change curve reaches a preset difference, determining a corresponding plurality of overpotentials; determining a plurality of impedances of the battery based on the determined plurality of overpotentials and the applied current; Obtaining a simulated change curve of the battery potential through a plurality of overpotentials, the battery equilibrium potential and a potential equation of the battery potential comprises: obtaining the potential equation, the potential equation being Ecell=Eocv(SOCave,T)+ηIR+ηact+ηconc, wherein Ecell is the battery potential, Eocv(SOCave,T) is a function of the battery equilibrium potential with respect to the state of charge and temperature, ηIR is the ohmic overpotential, ηact is the electrochemical reaction overpotential, and ηconc is the concentration difference overpotential; setting initial empirical values ​​for the ohmic overpotential, the electrochemical reaction overpotential and the concentration difference overpotential, and obtaining a simulated change curve of the battery potential through a plurality of battery equilibrium potentials; The method is applied to a model developer, and before obtaining a simulated change curve of a battery potential through a plurality of overpotentials, the battery equilibrium potential and the potential equation of a battery voltage, the method further includes: establishing a lumped battery physical field node corresponding to the battery in the model developer, wherein the lumped battery physical field node includes a battery equilibrium potential subnode and a voltage loss subnode; obtaining the plurality of overpotentials, the battery equilibrium potential and the potential equation of the battery potential through the lumped battery physical field node; generating a function of the battery equilibrium potential to the state of charge and the temperature according to the battery equilibrium potential under a plurality of states of charge and the corresponding temperature through the battery balance positioning subnode; determining the plurality of overpotentials through the voltage loss subnode; The step of bringing the simulated change curve close to the measured change curve by changing the values ​​of the multiple overpotentials includes: establishing an optimization node, and changing the values ​​of the multiple overpotentials according to the least square method through the optimization node to bring the simulated change curve close to the measured change curve; Before establishing the optimization node, the method also includes: defining the initial value of the global variable in the model developer; creating a parameter sub-node, wherein the parameter sub-node includes battery capacity, state of charge, and temperature.

2. The method according to claim 1, characterized in that When the difference between the simulated change curve and the measured change curve reaches a preset difference, determining the corresponding multiple overpotentials includes: Setting a calculation function in the model builder; Determine a plurality of simulated variation curves of overpotentials by operating the operation function in the model developer; The corresponding overpotential is determined by means of the simulated variation curve of the overpotential.

3. The method according to claim 2, characterized in that The simulation change curves of multiple overpotentials are determined by operating the operation function in the model developer, including: By calculating the calculation function in the model developer, a group diagram of voltage loss and load is obtained; The simulated variation curves of the plurality of overpotentials are determined according to the group of graphs.

4. A battery internal resistance determination device, characterized in that: include: An acquisition module, used to acquire the measured potential and applied current of the battery, and the battery equilibrium potential of the battery under multiple charge states during the process of measuring the DC internal resistance; An equation module, used for obtaining a simulated change curve of the battery potential through a plurality of overpotentials, the battery equilibrium potential and a potential equation of the battery potential, wherein the plurality of overpotentials include an ohmic overpotential, an electrochemical reaction overpotential and a concentration difference overpotential; Obtaining a simulated change curve of the battery potential through a plurality of overpotentials, the battery equilibrium potential and a potential equation of the battery potential comprises: obtaining the potential equation, the potential equation being Ecell=Eocv(SOCave,T)+ηIR+ηact+ηconc, wherein Ecell is the battery potential, Eocv(SOCave,T) is a function of the battery equilibrium potential with respect to the state of charge and temperature, ηIR is the ohmic overpotential, ηact is the electrochemical reaction overpotential, and ηconc is the concentration difference overpotential; setting initial empirical values ​​for the ohmic overpotential, the electrochemical reaction overpotential and the concentration difference overpotential, and obtaining a simulated change curve of the battery potential through a plurality of battery equilibrium potentials; Applied to a model developer, before obtaining a simulated change curve of the battery potential through a plurality of overpotentials, the battery equilibrium potential and the potential equation of the battery voltage, the battery internal resistance determination device is further used to: establish a lumped battery physical field node corresponding to the battery in the model developer, wherein the lumped battery physical field node includes a battery equilibrium potential subnode and a voltage loss subnode; obtain the plurality of overpotentials, the battery equilibrium potential and the potential equation of the battery potential through the lumped battery physical field node; generate a function of the battery equilibrium potential to the state of charge and the temperature according to the battery equilibrium potential under a plurality of states of charge and the corresponding temperature through the battery balance positioning subnode; determine the plurality of overpotentials through the voltage loss subnode; The step of bringing the simulated change curve close to the measured change curve by changing the values ​​of the multiple overpotentials includes: establishing an optimization node, and changing the values ​​of the multiple overpotentials according to the least square method through the optimization node to bring the simulated change curve close to the measured change curve; Before establishing the optimization node, the battery internal resistance determination device is also used to: define the initial value of the global variable in the model developer; create a parameter sub-node, wherein the parameter sub-node includes battery capacity, state of charge, and temperature; An optimization module, configured to bring the simulated change curve close to the measured change curve by changing the values ​​of the multiple overpotentials, wherein the measured change curve is a change curve of the measured potential versus time; A first determination module, configured to determine a plurality of corresponding overpotentials when a difference between the simulated change curve and the measured change curve reaches a preset difference; The second determination module is used to determine multiple impedances of the battery according to the determined multiple overpotentials and the applied current.

5. A computer storage medium, characterized in that: The computer storage medium includes a stored program, wherein when the program is executed, the device where the computer storage medium is located is controlled to execute the battery internal resistance determination method according to any one of claims 1 to 3.

6. A processor, characterized in that: The processor is used to run a program, wherein the program executes the battery internal resistance determination method according to any one of claims 1 to 3 when running.

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