A Modeling Method, Device, Equipment and Readable Storage Medium for an Energy Storage Battery Assembly

Abstract

The present invention discloses a method, device, equipment and readable storage medium for modeling an energy storage battery pack. The capacity, current, temperature, external voltage and power of a single battery cell are obtained, and based on the capacity, current, temperature, external voltage and power of the single battery cell, normal distribution functions of ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity are obtained; furthermore, the equivalent impedance and equivalent capacity of the battery pack are calculated as the grouped equivalent parameters to complete the modeling of the energy storage battery pack. By sampling and modeling the single cells of the battery pack and introducing an attenuation function to correct the model parameters of the battery pack under different operation years, the present invention can more truly reflect the battery state, realizes the modeling of the operation characteristics of the battery pack over the entire life cycle, and thus effectively supports the simulation analysis and operation and maintenance of the energy storage power station.

Description

Technical Field

[0001] The present invention belongs to the field of electric energy storage, and particularly relates to a method, device, equipment and readable storage medium for modeling an energy storage battery assembly. Background Art

[0002] In an energy storage system, a battery pack is formed by connecting battery cells in series and parallel. Its modeling process also needs to form a corresponding battery pack model by connecting in series and parallel based on the battery cell model. In the process of modeling a series battery pack, the combined modeling method and the sampling modeling method are commonly used to build the model. The combined modeling method is formed by connecting the Thévenin equivalent circuit simulation models of single energy storage batteries in series. Since in the energy storage battery pack, the working current of each single cell is the same, the battery cells operate independently of each other without mutual coupling effects. The output external voltage of each single cell is directly superimposed to obtain the output external voltage of the battery pack, and the polarization voltage of each single cell is directly superimposed to obtain the polarization voltage of the battery pack. In the combined modeling method, individual simulation operations and parameter dynamic identification need to be performed on each single cell, which ensures the high precision of the model, but at the same time brings a huge workload, the simulation model is more complex, and it occupies more computer resources and space. Therefore, it is difficult to realize the simulation of a large-scale series battery pack.

[0003] The sampling modeling method considers that the battery cells in the series-connected group model have the same cycling conditions and the same working current, that is, the model has a certain repeatability. The difference is only that the ohmic internal resistance, polarization voltage, polarization resistance, etc. of each battery cell in the battery pack are inconsistent. The parameter distribution law of the sample battery can be obtained by using the method of mathematical analysis, and then the same distribution is extended to the parameter distribution of the entire battery pack, and a battery parameter data table that meets the parameter distribution required in the series-connected group simulation model of the energy storage battery pack can be obtained, thus solving the problem of battery parameter identification in the series-connected group simulation model.

[0004] However, the process of obtaining the parameter distribution law by the sampling modeling method is based on the parameter sampling results of the sample battery at that time, and cannot reflect the attenuation process during the long-term operation of the battery pack, resulting in a deviation between the model and the actual system representation during actual application. Summary of the Invention

[0005] The purpose of the present invention is to propose an energy storage battery assembly modeling method, device, equipment and readable storage medium that can reflect the attenuation process of the energy storage battery. This method can reflect the changes in core parameters such as impedance and capacity during the long-term operation of the battery pack, and provide a reliable model support for the operation and maintenance of the energy storage power station.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0007] An energy storage battery assembly modeling method, comprising the following steps:

[0008] Obtain the battery parameters of each battery cell, and calculate the normal distribution functions corresponding to the ohmic resistance, polarization resistance, polarization capacitance, and capacity one by one according to the battery parameters; the battery parameters include capacity, current, temperature, external voltage, and power.

[0009] Generate the ohmic resistance, polarization resistance, polarization capacitance, and capacity of each battery cell in the t-th year according to the normal distribution functions corresponding to the ohmic resistance, polarization resistance, polarization capacitance, and capacity one by one.

[0010] Calculate the equivalent impedance and equivalent capacity of the battery pack according to the ohmic resistance, polarization resistance, polarization capacitance, and capacity of each battery cell in the t-th year, and use the equivalent impedance and equivalent capacity of the battery pack as the grouped equivalent parameters.

[0011] The process of determining the grouped equivalent parameters is the process of modeling the energy storage battery pack.

[0012] Furthermore, calculating the normal distribution functions corresponding to the ohmic resistance, polarization resistance, polarization capacitance, and capacity one by one according to the battery parameters includes the following steps:

[0013] Obtain the ohmic resistance, polarization resistance, polarization capacitance, and capacity of each battery cell according to the capacity, current, temperature, external voltage, and power.

[0014] Conduct a normality test on the ohmic resistance, polarization resistance, polarization capacitance, and capacity of each battery cell, and calculate the average value and standard deviation of the ohmic resistance, polarization resistance, polarization capacitance, and capacity.

[0015] Obtain the normal distribution functions corresponding to the ohmic resistance, polarization resistance, polarization capacitance, and capacity one by one according to the average value and standard deviation of the ohmic resistance, polarization resistance, polarization capacitance, and capacity.

[0016] Furthermore, the normal distribution functions corresponding to the ohmic resistance, polarization resistance, polarization capacitance, and capacity one by one are:

[0017]

[0018]

[0019]

[0020]

[0021] Among them, is the average value of the ohmic resistance, is the average value of the polarization resistance, is the average value of the polarization capacitance, μ Q is the average value of the capacity, is the standard deviation of the ohmic internal resistance, is the standard deviation of the polarization internal resistance, is the standard deviation of the polarization capacitance, σ Q is the standard deviation of the capacity, R0 is the ohmic internal resistance, R p is the polarization internal resistance, C P is the polarization capacitance, Q is the capacity.

[0022] Further, according to the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity of each battery cell in the t-th year, calculate the equivalent impedance and equivalent capacity of the battery pack, including the following steps:

[0023] Modify the normal distribution functions corresponding to the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity to obtain the modified normal distribution functions;

[0024] Generate the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity of each battery cell in the t-th year according to the modified normal distribution functions;

[0025] The modified normal distribution functions are:

[0026]

[0027]

[0028]

[0029]

[0030] where R0(t) is the modified ohmic internal resistance, R p (t) is the modified polarization internal resistance, C P (t) is the modified polarization capacitance, Q(t) is the modified capacity, is the exponential decay function of the ohmic internal resistance, is the exponential decay function of the polarization internal resistance, is the exponential decay function of the polarization capacitance, ξ Q is the exponential decay function of the capacity.

[0031] Further, the exponential decay function of the ohmic internal resistance, the exponential decay function of the polarization internal resistance, the exponential decay function of the polarization capacitance and the exponential decay function of the capacity are calculated by the following formula:

[0032]

[0033]

[0034]

[0035]

[0036] Among them, y R0 is a parameter of the ohmic internal resistance attenuation process, y RP is a parameter of the polarization internal resistance attenuation process, y CP is a parameter of the polarization capacitance attenuation process, y Q is a parameter of the capacity attenuation process.

[0037] Furthermore, the equivalent impedance of the battery pack is calculated by the following formula:

[0038]

[0039] Among them, Z p (t) is the equivalent impedance of the battery pack, i is the label of the battery cell, n is the total number of battery cells, j is the imaginary symbol, w is the circuit frequency, R 0i (t) is the ohmic internal resistance of the i-th battery cell in the t-th year, R pi (t) is the polarization internal resistance of the i-th battery cell in the t-th year, C pi (t) is the polarization capacitance of the i-th battery cell in the t-th year.

[0040] Furthermore, the equivalent capacity of the battery pack is calculated by the following formula:

[0041] Q (series) = min{Q1(t), Q2(t),......Q n (t)}

[0042] Among them, Q (series) is the equivalent capacity of the battery pack, Q1(t) is the capacity of the battery cell labeled 1, Q2(t) is the capacity of the battery cell labeled 2, Q n (t) is the capacity of the battery cell labeled n.

[0043] An energy storage battery modeling device includes:

[0044] A normal distribution function acquisition module, configured to obtain battery parameters of battery cells, and calculate normal distribution functions corresponding to the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity according to the battery parameters; the battery parameters include capacity, current, temperature, external voltage, and power;

[0045] A generation module, configured to generate the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity of each battery cell in the t-th year according to the normal distribution functions corresponding to the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity;

[0046] The energy storage battery module modeling module is used to calculate the equivalent impedance and equivalent capacity of the battery pack according to the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity of each battery cell in the t-th year, and use the equivalent impedance and equivalent capacity of the battery pack as the grouped equivalent parameters;

[0047] The process of determining the grouped equivalent parameters is the process of energy storage battery module modeling.

[0048] Optionally, the generating module is specifically used for:

[0049] Obtain the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity of each battery cell according to the capacity, current, temperature, external voltage and power;

[0050] Conduct a normality test on the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity of each battery cell, and calculate the average value and standard deviation of the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity;

[0051] Obtain the normal distribution functions corresponding to the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity one by one according to the average value and standard deviation of the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity.

[0052] A computer device, the computer device includes a memory and a processor, and a computer program capable of running on the processor is stored on the memory. When the computer program is executed by the processor, the energy storage battery module modeling method described above is implemented.

[0053] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor is caused to execute the energy storage battery module modeling method.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] The present invention constructs a model for the entire battery module based on the normal distribution by extracting several battery cells. Compared with the existing method of analyzing all battery cells in the module, when carrying out the modeling process of the energy storage power station, the modeling time can be effectively shortened (a large-scale energy storage power station includes tens of thousands of cells), and the work efficiency can be improved.

[0056] Furthermore, the present invention samples and models the battery cells of the battery pack, and introduces an attenuation function to correct the model parameters of the battery pack under different operation years, which can more truly reflect the dynamic state of core parameters such as battery impedance and capacity at different operation times, and realizes the modeling of the full-life cycle operation characteristics of the battery pack, thus effectively supporting the simulation analysis and operation and maintenance of the energy storage power station. Description of the Drawings

[0057] Figure 1 It is the flow chart of the energy storage battery module modeling considering the attenuation probability sampling of the present invention.

[0058] Figure 2 It is the schematic diagram of the energy storage battery module modeling device of the present invention. Specific embodiments

[0059] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0060] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0061] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media including storage devices.

[0062] In the present invention, "module", "device", "system", etc. refer to relevant entities applied to a computer, such as hardware, a combination of hardware and software, software, or software in execution. Specifically, for example, an element may be, but is not limited to, a process running on a processor, a processor, an object, an executable element, an execution thread, a program, and / or a computer. Also, an application program or a script program running on a server, and the server may both be elements. One or more elements may be in a process and / or thread in execution, and the elements may be localized on one computer and / or distributed between two or more computers, and may be run by various computer-readable media. The elements may also communicate through local and / or remote processes according to a signal having one or more data packets, for example, a signal from data interacting with another element in a local system, a distributed system, and / or through a network on the Internet and interacting with other systems.

[0063] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising" and "including" not only include those elements, but also other elements not explicitly listed, or elements inherent to such a process, method, article, or device. Without further limitation, an element defined by the statement "comprising..." does not exclude the existence of additional identical elements in the process, method, article, or device that includes the said element.

[0064] See Figure 1 , a method for modeling an energy storage battery module considering decay probability sampling according to the present invention includes the following steps:

[0065] Step 1, first extract several battery monomers in the same string and conduct parameter distribution statistical analysis to obtain normal distribution functions corresponding one-to-one to the ohmic internal resistance R0, polarization internal resistance R P , polarization capacitance C P and capacity Q;

[0066] Randomly extract several (generally more than 10) battery monomers in the same string, and use equipment such as a high and low temperature box, a battery charge and discharge test system, a data acquisition system, and a computer system to conduct charge and discharge tests on the batteries, and collect the parameters of the energy storage batteries to be measured during the experiment, including capacity, current, temperature, external voltage, and power, etc.

[0067] By extracting several battery monomers according to the present invention, compared with the existing method of analyzing all battery monomers (some energy storage batteries include tens of thousands of monomers), the time is shortened and the work efficiency is improved.

[0068] Use the Thevenin model to model the battery monomers, and identify the ohmic internal resistance R0, polarization internal resistance R P , polarization capacitance C P and capacity Q of each battery monomer through the collected capacity, current, temperature, external voltage, and power.

[0069] Perform normality tests on the ohmic internal resistance R0, polarization internal resistance R P , polarization capacitance C P and capacity Q of each sampled battery monomer, calculate the average value and standard deviation of the ohmic internal resistance R0, polarization internal resistance R P , polarization capacitance C P and capacity Q, and obtain normal distribution functions corresponding one-to-one to the ohmic internal resistance R0, polarization internal resistance R P , polarization capacitance C P and capacity Q according to the average value and standard deviation.

[0070]

[0071]

[0072]

[0073]

[0074] Among them, is the average value of the ohmic internal resistance, is the average value of the polarization internal resistance, is the average value of the polarization capacitance, μ Q is the average value of the capacity, is the standard deviation of the ohmic internal resistance, is the standard deviation of the polarization internal resistance, is the standard deviation of the polarization capacitance, σ Q is the standard deviation of the capacity, R0 is the ohmic internal resistance, R p is the polarization internal resistance, C P is the polarization capacitance, Q is the capacity.

[0075] Step 2, correct the normal distribution functions corresponding one by one to the ohmic internal resistance R0, polarization internal resistance R P , polarization capacitance C P and capacity Q according to the exponential decay function.

[0076] Construct the following exponential decay function, where t is the number of years of operation, and the parameters y R0 , y RP , y CP and y Q are obtained by fitting the data of the typical decay test curve of the battery provided by the manufacturer or measured in the laboratory.

[0077]

[0078]

[0079]

[0080]

[0081] Among them, is the exponential decay function of the ohmic internal resistance, is the exponential decay function of the polarization internal resistance, is the exponential decay function of the polarization capacitance, ξ Q is the exponential decay function of the capacity, y R0 is the parameter of the ohmic internal resistance decay process, y RP is the parameter of the polarization internal resistance decay process, y CPis a parameter for the polarization capacitance decay process, y Q is a parameter for the capacity decay process.

[0082] Use the constructed exponential decay function to correct the normal distribution function corresponding one-to-one to the ohmic internal resistance R0, polarization internal resistance R P , polarization capacitance C P and capacity Q of the t-th year, and obtain the corrected normal distribution function;

[0083]

[0084]

[0085]

[0086]

[0087] Among them, R0(t) is the corrected ohmic internal resistance, R p (t) is the corrected polarization internal resistance, C P (t) is the corrected polarization capacitance, and Q(t) is the corrected capacity.

[0088] Step 3, generate the battery cell parameters of this string according to the corrected normal distribution function.

[0089] Use the random number generator in Excel or Matlab software, based on the corrected normal distribution function obtained in Step 2, to generate the ohmic internal resistance R 0i (t), polarization internal resistance R pi (t), polarization capacitance C pi (t) and capacity Q(t) of each battery cell of this string in the t-th year.

[0090] Step 4, calculate the equivalent parameters of the group and complete the model construction according to the battery cell parameters of the string.

[0091] Use the following formula to calculate the equivalent parameters of the battery string, including the equivalent impedance of the battery pack and the equivalent capacity of the battery pack. Among them, the equivalent impedance of the battery pack is equal to the sum of the impedances of each battery cell, and the equivalent capacity of the battery pack is the minimum capacity of the battery cell.

[0092]

[0093] Q (series) =min{Q1(t),Q2(t),……Q n (t)}

[0094] Among them, Z p (t) is the equivalent impedance of the battery pack, i is the battery cell label, n is the total number of battery cells, j is the imaginary symbol, w is the circuit frequency, R0i R(t) is the ohmic internal resistance of the i-th battery cell in the t-th year. pi C(t) is the polarization internal resistance of the i-th battery cell in the t-th year. pi C(t) is the polarization capacitance of the i-th battery cell in the t-th year. (series) Q is the equivalent capacity of the battery pack, Q1(t) is the capacity of the battery cell labeled 1, Q2(t) is the capacity of the battery cell labeled 2, and Q n (t) is the capacity of the battery cell labeled n.

[0095] Taking the equivalent impedance and equivalent capacity of the battery pack as the grouped equivalent parameters, the energy storage battery pack is equivalently modeled according to the grouped equivalent parameters, and the energy storage battery pack modeling is completed. That is to say, the process of determining the grouped equivalent parameters is the process of energy storage battery pack modeling.

[0096] The above two model parameters, namely the equivalent impedance and equivalent capacity of the battery pack, can fully reflect the impedance characteristics and charge-discharge capabilities of the battery pack during the charge-discharge process in different operation years, thus realizing the effective establishment of the equivalent model of the battery pack.

[0097] In the present invention, a model of the entire battery module is constructed based on the normal distribution by extracting several battery cells. Compared with the existing method of analyzing all battery cells in the module, during the process of building the energy storage power station model, the modeling time can be effectively shortened (a large-scale energy storage power station includes tens of thousands of cells), and the work efficiency can be improved.

[0098] In addition, in the present invention, by sampling and modeling the battery cells of the battery pack and introducing an attenuation function to correct the model parameters of the battery pack in different operation years, the dynamic states of core parameters such as battery impedance and capacity at different operation times can be more realistically reflected, realizing the modeling of the full-life cycle operation characteristics of the battery pack, thereby effectively supporting the simulation analysis and operation and maintenance of the energy storage power station.

[0099] In addition, the embodiment of the present application also provides an energy storage battery pack modeling device, as Figure 2 shown, including: a normal distribution function obtaining module, a generating module, and an energy storage battery pack modeling module.

[0100] The normal distribution function obtaining module is used to obtain the battery parameters of the battery cells and calculate the normal distribution functions corresponding to the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity one by one according to the battery parameters; the battery parameters include capacity, current, temperature, external voltage, and power.

[0101] The generating module is used to generate the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity of each battery cell in the t-th year according to the normal distribution functions corresponding to the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity one by one.

[0102] The energy storage battery module modeling module is used to calculate the equivalent impedance and equivalent capacity of the battery pack according to the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity of each battery cell in the t-th year, and use the equivalent impedance and equivalent capacity of the battery pack as the grouped equivalent parameters;

[0103] The process of determining the grouped equivalent parameters is the process of energy storage battery module modeling.

[0104] Optionally, the generation module is specifically used for:

[0105] Obtain the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity of each battery cell according to the capacity, current, temperature, external voltage and power;

[0106] Perform a normality test on the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity of each battery cell, and calculate the average value and standard deviation of the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity;

[0107] Obtain the normal distribution functions corresponding to the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity one by one according to the average value and standard deviation of the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity.

[0108] In addition, an embodiment of the present application further provides a computer device, which includes a memory and a processor. A computer program capable of running on the processor is stored on the memory. When the computer program is executed by the processor, the energy storage battery module modeling method described above is implemented. The computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function.

[0109] In addition, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which when executed by a processor causes the processor to execute the energy storage battery module modeling method as described above. The readable storage medium is specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. And, in this storage space, one or more instructions suitable for being loaded and executed by the processor are also stored, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor.

[0110] In the present invention, the attenuation process is introduced into the modeling process to realize the characterization of the long-term operation characteristics of the energy storage battery pack.

[0111] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0112] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0113] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one process Figure 1 one process or a plurality of processes and / or boxes Figure 1 specified in one box or a plurality of boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process Figure 1 one process or a plurality of processes and / or boxes Figure 1 specified in one box or a plurality of boxes.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent substitutions can still be made to the specific implementation manners of the present invention, and any modification or equivalent substitution that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A modeling method for an energy storage battery assembly, characterized in that, Including the following steps: Obtain the battery parameters of each battery cell, and calculate the normal distribution functions corresponding one-to-one to the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity according to the battery parameters; the battery parameters include capacity, current, temperature, external voltage, and power; Generate the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity of each battery cell in the t-th year according to the normal distribution functions corresponding one-to-one to the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity; Calculate the equivalent impedance and equivalent capacity of the battery pack according to the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity of each battery cell in the t-th year, and use the equivalent impedance and equivalent capacity of the battery pack as the grouped equivalent parameters; The process of determining the grouped equivalent parameters is the process of modeling the energy storage battery pack; Calculating the equivalent impedance and equivalent capacity of the battery pack according to the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity of each battery cell in the t-th year includes the following steps: Modify the normal distribution functions corresponding one-to-one to the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity to obtain the modified normal distribution functions; Generate the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity of each battery cell in the t-th year according to the modified normal distribution functions; The modified normal distribution function is: Among them, is the corrected ohmic internal resistance, is the corrected polarization internal resistance, is the corrected polarization capacitance, is the corrected capacitance, is the exponential decay function of the ohmic internal resistance, is the exponential decay function of the polarization internal resistance, is the exponential decay function of the polarization capacitance, is the exponential decay function of the capacitance; The ohmic internal resistance exponential decay function, the polarization internal resistance exponential decay function, the polarization capacitance exponential decay function, and the capacity exponential decay function are calculated by the following formula: Among them, y R0 is the parameter of the ohmic internal resistance attenuation process, y RP is the parameter of the polarization internal resistance attenuation process, y CP is the parameter of the polarization capacitance attenuation process, y Q is the parameter of the capacity attenuation process.

2. The modeling method of an energy storage battery assembly according to claim 1, characterized in that Calculating the normal distribution functions corresponding one-to-one to the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity according to the battery parameters includes the following steps: Obtain the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity of each battery cell according to the capacity, current, temperature, external voltage, and power; Perform a normality test on the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity of each battery cell, and calculate the average value and standard deviation of the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity; Obtain the normal distribution functions corresponding one-to-one to the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity according to the average value and standard deviation of the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity; 3. A modeling method for an energy storage battery assembly according to claim 2, characterized in that, The normal distribution functions corresponding one-to-one to the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity are: Among them, is the average value of the ohmic internal resistance, is the average value of the polarization internal resistance, is the average value of the polarization capacitance, is the average value of the capacity, is the standard deviation of the ohmic internal resistance, is the standard deviation of the polarization internal resistance, is the standard deviation of the polarization capacitance, is the standard deviation of the capacity, is the ohmic internal resistance, is the polarization internal resistance, is the polarization capacitance, is the capacity.

4. A modeling method for an energy storage battery assembly according to claim 1, characterized in that The equivalent impedance of the battery pack is calculated by the following formula: wherein, is the equivalent impedance of the battery pack, i is the label of the battery cell, n is the total number of battery cells, is the imaginary symbol, w is the circuit frequency, is the ohmic internal resistance of the i-th battery cell in the t-th year, is the polarization internal resistance of the i-th battery cell in the t-th year, is the polarization capacitance of the i-th battery cell in the t-th year.

5. A modeling method for an energy storage battery assembly according to claim 1, characterized in that, The equivalent capacity of the battery pack is calculated by the following formula: Among them, is the equivalent capacity of the battery pack, is the capacity of the battery cell labeled 1, is the capacity of the battery cell labeled 2, is the capacity of the battery cell labeled n.

6. An energy storage battery assembly modeling device, characterized in that Including: A normal distribution function acquisition module, configured to obtain the battery parameters of each battery cell, and calculate the normal distribution functions corresponding one-to-one to the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity according to the battery parameters; the battery parameters include capacity, current, temperature, external voltage, and power; A generation module, configured to generate the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity of each battery cell in the t-th year according to the normal distribution functions corresponding one-to-one to the ohmic internal resistance, polarization internal resistance, polarization capacitance, and capacity; The energy storage battery module modeling module is used to calculate the equivalent impedance and equivalent capacity of the battery pack according to the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity of each battery cell in the t-th year, and use the equivalent impedance and equivalent capacity of the battery pack as the grouped equivalent parameters; The process of determining the grouped equivalent parameters is the process of energy storage battery module modeling; Calculating the equivalent impedance and equivalent capacity of the battery pack according to the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity of each battery cell in the t-th year includes the following steps: Modify the normal distribution functions corresponding to the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity one by one to obtain the modified normal distribution functions; Generate the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity of each battery cell in the t-th year according to the modified normal distribution functions; The modified normal distribution function is: Among them, is the corrected ohmic internal resistance, is the corrected polarization internal resistance, is the corrected polarization capacitance, is the corrected capacitance, is the exponential decay function of the ohmic internal resistance, is the exponential decay function of the polarization internal resistance, is the exponential decay function of the polarization capacitance, is the exponential decay function of the capacitance; The ohmic internal resistance exponential decay function, the polarization internal resistance exponential decay function, the polarization capacitance exponential decay function and the capacity exponential decay function are calculated by the following formula: Among them, y R0 is the parameter of the ohmic internal resistance attenuation process, y RP is the parameter of the polarization internal resistance attenuation process, y CP is the parameter of the polarization capacitance attenuation process, y Q is the parameter of the capacity attenuation process.

7. The device according to claim 6, characterized in that, The generation module is used for: Obtain the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity of each battery cell according to the capacity, current, temperature, external voltage and power; Conduct a normality test on the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity of each battery cell, and calculate the average value and standard deviation of the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity; Obtain the normal distribution functions corresponding to the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity one by one according to the average value and standard deviation of the ohmic internal resistance, polarization internal resistance, polarization capacitance and capacity.

8. A computer device, characterized in that, The computer device includes a memory and a processor. A computer program capable of running on the processor is stored on the memory. When the computer program is executed by the processor, the energy storage battery module modeling method described in any one of claims 1-5 is implemented.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program. When the computer program is executed by the processor, the processor executes the energy storage battery module modeling method described in any one of claims 1-5.

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