Modeling method for internal resistance parameters of battery, and related apparatus

Abstract

The embodiments of the present application relate to the technical field of batteries. Provided are a modeling method for internal resistance parameters of a battery, and a related apparatus. The method comprises: acquiring a data set for parameter modeling, and a second-order parameter model corresponding to second-order parameters in a second-order equivalent circuit model, wherein the data set comprises the temperatures, voltages and currents of a battery under different operating conditions, the second-order equivalent circuit model is used for simulating the operating conditions of the battery, and the second-order parameters are internal resistance parameters of the battery; and on the basis of the data set and the second-order parameter model, modeling the second-order parameters. In the method, a table estimation problem of second-order parameters is converted into a surface parameter estimation problem by means of an established second-order parameter model, such that the second-order parameters can be quickly modeled, thereby reducing the difficulty of acquiring the second-order parameters, and improving the efficiency.

Description

Battery internal resistance parameter modeling method and related device

[0001] The present application claims priority to the Chinese patent application No. 202411311137.9, filed on September 19, 2024, and entitled "Battery internal resistance parameter modeling method and related device", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the technical field of battery, in particular to a battery internal resistance parameter modeling method and related device. BACKGROUND

[0003] Lithium battery can be regarded as an electrochemical system composed of electrodes, electrolyte and separator. In order to simplify the complexity of electrochemical reaction, lithium battery can be abstracted into an equivalent circuit model, so as to better understand its electrical performance.

[0004] The second-order equivalent circuit model is one of the most widely used lithium battery equivalent circuit models. In the process of using the second-order equivalent circuit model, the second-order parameters R2 and τ2 used to describe the concentration polarization caused by solid-phase diffusion need to be calibrated. However, the second-order parameters R2 and τ2 have high nonlinearity under different battery working conditions. How to quickly and accurately obtain the second-order parameters R2 and τ2 has become a problem to be solved by those skilled in the art. SUMMARY

[0005] The embodiments of the present application provide a battery internal resistance parameter modeling method and related device, which can realize the rapid acquisition of the second-order parameters.

[0006] In a first aspect, the embodiments of the present application provide a battery internal resistance parameter modeling method, comprising:

[0007] obtaining a data set for parameter modeling and a second-order parameter model corresponding to a second-order parameter in a second-order equivalent circuit model; the data set comprising the temperature, voltage and current of the battery under different working conditions, the second-order equivalent circuit model being used to simulate the running condition of the battery, and the second-order parameter being an internal resistance parameter of the battery;

[0008] modeling the second-order parameter according to the data set and the second-order parameter model.

[0009] In some embodiments, the modeling the second-order parameter according to the data set and the second-order parameter model comprises:

[0010] obtaining the real value of the state of charge of the battery according to the data set;

[0011] solving the target parameter in the second-order parameter model according to the data set, the second-order parameter model, and the true value, the target parameter being a parameter in the second-order parameter model other than the second-order parameter;

[0012] applying the solved target parameter to the second-order parameter model to obtain the modeled second-order parameter.

[0013] In some embodiments, the solving the target parameter in the second-order parameter model according to the data set, the second-order parameter model, and the true value comprises:

[0014] obtaining a preset parameter range corresponding to the target parameter;

[0015] obtaining a predicted value of the state of charge of the battery according to the preset parameter range, the data set, and the second-order parameter model;

[0016] determining a fitness of the state of charge according to the predicted value and the true value;

[0017] updating the target parameter according to the fitness, and iteratively performing the step of determining the fitness of the state of charge until a preset number of iterations is reached;

[0018] applying the target parameter updated at the preset number of iterations as the solved target parameter.

[0019] In some embodiments, the obtaining a predicted value of the state of charge of the battery according to the preset parameter range, the data set, and the second-order parameter model comprises:

[0020] randomly determining a value of the target parameter from the preset parameter range;

[0021] applying the determined value of the target parameter to the second-order parameter model to obtain the second-order parameter;

[0022] applying the second-order parameter and the data set to an estimation model to obtain the predicted value of the state of charge.

[0023] In some embodiments, the method further comprises:

[0024] evaluating the modeled second-order parameter for consistency;

[0025] if the evaluation for consistency fails, adjusting the data set and / or the preset parameter range, and re-modeling the second-order parameter.

[0026] In some embodiments, the evaluating the modeled second-order parameter for consistency comprises:

[0027] modeling the second-order parameter at least twice to obtain at least two groups of the target parameters;

[0028] respectively inputting real values of the battery temperature and the state of charge under different working conditions into the second-order parameter model under at least two groups of the target parameters respectively to obtain at least two groups of parameter sets of the second-order parameter;

[0029] performing consistency evaluation on the second-order parameter according to a consistency correlation coefficient between the at least two groups of parameter sets.

[0030] In some embodiments, the consistency evaluation on the second-order parameter according to the consistency correlation coefficient between the first parameter set and the second parameter set comprises:

[0031] obtaining a consistency correlation coefficient between any two groups of parameter sets;

[0032] if any of the consistency correlation coefficients is greater than a preset threshold, determining that the consistency evaluation fails.

[0033] In a second aspect, an embodiment of the present application provides a pool internal resistance parameter modeling device, comprising:

[0034] a first obtaining module configured to obtain a data set for parameter modeling and a second-order parameter model corresponding to a second-order parameter in a second-order equivalent circuit model; the data set comprises the temperature, the voltage and the current of the battery under different working conditions, the second-order equivalent circuit model is used to simulate the working condition of the battery, and the second-order parameter is an internal resistance parameter of the battery;

[0035] a second obtaining module configured to obtain a real value of the state of charge of the battery according to the data set;

[0036] a modeling module configured to model the second-order parameter according to the data set, the second-order parameter model and the real value.

[0037] In a third aspect, the present application provides an electronic device, comprising a memory and a processor;

[0038] the memory is configured to store computer instructions, and the processor is configured to run the computer instructions stored in the memory to implement the method of any one of the first aspect.

[0039] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method of any one of the first aspect.

[0040] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which is executed by a processor to implement the method of any one of the first aspect.

[0041] The battery internal resistance parameter modeling method and related device provided by the embodiments of the present application, by obtaining a data set for parameter modeling, and a second-order parameter model corresponding to a second-order parameter in a second-order equivalent circuit model; the data set includes the temperature, voltage and current of the battery under different working conditions, the second-order equivalent circuit model is used to simulate the operating conditions of the battery, and the second-order parameter is the internal resistance parameter of the battery; the second-order parameter is modeled according to the data set and the second-order parameter model. The above method converts the table estimation problem of the second-order parameter into the surface parameter estimation problem through the established second-order parameter model, can quickly realize the modeling of the second-order parameter, reduce the difficulty of the second-order parameter modeling, and improve the modeling efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0042] FIG. 1 is a flow diagram of a battery internal resistance parameter modeling method according to an embodiment of the present application;

[0043] FIG. 2 is a flow diagram of a battery internal resistance parameter modeling method according to an embodiment of the present application;

[0044] FIG. 3 is a flow diagram of a consistency evaluation method according to an embodiment of the present application;

[0045] FIG. 4 is a schematic diagram of the effect of battery estimation according to an embodiment of the present application;

[0046] FIG. 5 is a structural diagram of a battery internal resistance parameter modeling device according to an embodiment of the present application;

[0047] FIG. 6 is a structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme of the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0049] In the embodiments of the present application, the same items or similar items with basically the same functions and effects are distinguished by using "first", "second", etc., without limiting the sequence. Those skilled in the art can understand that "first", "second", etc. do not limit the quantity and execution sequence, and "first", "second", etc. also do not necessarily mean different.

[0050] It should be noted that in the embodiments of the present application, the words such as "exemplary" or "for example" are used to mean example, illustration, or description. Any embodiment or design solution described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of "exemplary" or "for example" is intended to present the relevant concept in a specific manner.

[0051] A lithium battery can be regarded as an electrochemical system composed of electrodes, electrolytes and separators. In order to simplify the complexity of electrochemical reactions, a lithium battery can be abstracted into an equivalent circuit model, so as to better understand its electrical performance. The second-order equivalent circuit model is a common equivalent circuit model of lithium battery. It is composed of two capacitors and two resistors, which respectively represent the internal resistance of the battery, the electrolyte dissipation and the capacitance between the battery plates. Through modeling of circuit parameters and fitting of voltage-current curves, the equivalent circuit model of the lithium battery can be obtained, and the accuracy of the model can be further improved through parameter adjustment.

[0052] The second-order equivalent circuit model describes the terminal voltage U t (t) as:

[0053] Wherein, U ocv is the open circuit voltage, I is the circuit current, R0 is used to describe Ohmic polarization, the first-order parameter R1, τ1 is used to describe electrochemical polarization, and the second-order parameter R2, τ2 is used to describe the concentration polarization caused by solid-phase diffusion. Wherein, the first-order parameter and the second-order parameter can be called as the internal resistance parameter of the battery.

[0054] Since the second-order parameter R2, τ2 is influenced by the state of charge (SOC), temperature T and discharge rate C, it has high nonlinearity.

[0055] In the related art, the R2, τ2 is usually determined by table lookup difference. In order to ensure the interpolation accuracy, the table composed of three dimensions of state of charge SOC, temperature T and discharge rate C needs to be finely divided, resulting in a large number of variables to be optimized. The too large variables to be optimized makes the optimization process easy to fall into local optimum, and it is difficult to guarantee the repeatability and fast convergence of the second-order parameter estimation, resulting in difficulty in modeling the second-order parameter and low efficiency.

[0056] Therefore, the embodiments of the present application provide a battery internal resistance parameter modeling method and related device. Through the second-order parameter model established, the table estimation problem in the second-order parameter modeling process is converted into a surface parameter estimation problem, the second-order parameter can be quickly modeled, the difficulty of obtaining the second-order parameter is reduced, and the efficiency is improved.

[0057] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific examples. The following specific examples can be implemented independently or in combination, and the same or similar concepts or processes can not be described again in some examples.

[0058] FIG. 1 is a flowchart of a battery internal resistance parameter modeling method according to an embodiment of the present application. The execution subject of the embodiment of the present application can be a computing platform, a server, a processor, etc. with data processing capability. Taking the computing platform as an example, as shown in FIG. 1, the battery internal resistance parameter modeling method includes the following steps:

[0059] S101, obtaining a data set for parameter modeling, and a second-order parameter model corresponding to a second-order parameter in a second-order equivalent circuit model.

[0060] In some embodiments, the data set includes the temperature, voltage and current of the battery under different working conditions. For example, the battery pack is subjected to light-duty vehicle driving cycle, light-duty vehicle driving test procedure, and cycle simulation experiment under cycle test working condition at-20℃, 0℃, 25℃ and 40℃, respectively. The temperature, voltage and current of each cell in the battery pack are recorded at a sampling frequency of 1 Hz or more to obtain the data set.

[0061] Optionally, at least two groups of data are tested at the same temperature to reduce the probability of deviation of the subsequent estimation results caused by data mutation.

[0062] The second-order parameter model is a model pre-established for characterizing the second-order parameter. For example, the second-order parameter model can be as follows:

[0063] When the discharge rate is a small current rate, the second-order parameter model can be as follows:

[0064] When the discharge rate is a large current rate, the second-order parameter model can be as follows:

[0065] Wherein, T0 is the reference temperature, C is the current rate, and a1-a33 are parameters to be optimized in the second-order parameter model.

[0066] S102, modeling the second-order parameter according to the data set and the second-order parameter model.

[0067] In some embodiments, modeling the second-order parameter (which can also be referred to as calibrating the second-order parameter) can refer to solving the parameters to be optimized (referred to as target parameters) in the established second-order parameter model to obtain the expression of the second-order parameter model. That is, the function expression corresponding to the second-order parameter is determined.

[0068] When the computing platform obtains the data set and the second-order parameter model, a heuristic algorithm can be used to iteratively solve a target parameter in the second-order parameter model to obtain the target parameter, and the solved target parameter is brought into the second-order parameter model to determine a function expression corresponding to the second-order parameter.

[0069] The process of modeling the second-order parameter according to the data set and the second-order parameter model will be described below in conjunction with FIG. 2. As shown in FIG. 2, the process includes the following steps:

[0070] S201. Obtain a true value of the state of charge of the battery according to the data set.

[0071] In some embodiments, when the computing platform obtains the data set, an ampere-hour integration method can be used to process the data collected at each sampling point to obtain an SOC value corresponding to each sampling point, i.e., the true value of the state of charge of the battery. The ampere-hour integration method is a basic method for measuring the capacity of a battery, which calculates the SOC by accumulating the integral of the battery current. The specific implementation of obtaining the true value of the SOC using the ampere-hour integration method can refer to the implementation in the prior art, which will not be described here.

[0072] S202. Solve a target parameter in the second-order parameter model according to the data set, the second-order parameter model, and the true value; the target parameter is a parameter in the second-order parameter model other than the second-order parameter.

[0073] In some embodiments, the computing platform can use a heuristic algorithm to solve the target parameter in the second-order parameter model according to the data set, the second-order parameter model, and the true value.

[0074] For example, solving the target parameter in the second-order parameter model using a heuristic algorithm can include the following steps:

[0075] A1. Obtain a preset parameter range corresponding to the target parameter.

[0076] The preset parameter range can be a value range of R2 and τ2 estimated according to prior knowledge, and a value range of a1 to a 33 estimated on this basis.

[0077] For example, the value range of a1 to a 33 may be as follows:

[0078] A2. Obtain a predicted value of the state of charge of the battery according to the preset parameter range, the data set, and the second-order parameter model.

[0079] In some embodiments, the computing platform can obtain the predicted value in the following manner.

[0080] For example, a heuristic algorithm is selected, and the heuristic algorithm is used to randomly determine the value of the target parameter from the preset parameter range; the determined value of the target parameter is brought into the second-order parameter model to obtain the second-order parameter; and the second-order parameter and the data set are brought into the estimation model to obtain the predicted value of the state of charge.

[0081] Taking the GWO algorithm as an example, the population size is set to M (for example, 30), the number of iterations is set to N times (for example, 1000 times), and the 33 target parameters are randomly initialized in the value range of the target parameter to generate M groups of initial parameters. Each group of initial parameters includes 33 target parameters.

[0082] For any group of parameters (any population), the 33 target parameters are brought into the second-order parameter model to obtain the second-order parameter. The second-order parameter and the data set are brought into the pre-established SOC estimation model to obtain the predicted value of the SOC of each sampling point output by the SOC estimation model. The SOC estimation model can use a Kalman filter estimation model.

[0083] A3, according to the predicted value and the true value, determining the fitness of the state of charge.

[0084] For any population, when the computing platform obtains the predicted value and the true value of the SOC corresponding to each sampling point, the fitness of the SOC can be obtained in the following manner.

[0085] wherein soc(i) is the SOC value of the ith sample point obtained by ampere-hour integration, is the SOC value of the ith sample point estimated by the estimation model under the given second-order model parameter, N is the number of samples used for parameter estimation. The max(·) term represents the maximum value in the sequence of absolute errors.

[0086] A4, updating the target parameter according to the fitness, and iteratively performing the step of determining the fitness of the state of charge until a preset number of iterations is reached.

[0087] In some embodiments, taking the GWO algorithm as an example, when the computing platform obtains the fitness of the SOC corresponding to each population, it sorts and classifies the M populations according to the fitness, and updates the target parameters according to the sorted and classified populations and the parameter update rules of the GWO algorithm. The specific implementation of the update can refer to existing update methods, which will not be elaborated here. It should be understood that if other heuristic algorithms (e.g., genetic algorithms, simulated annealing algorithms) are used, the update method corresponding to that algorithm will be adopted for updating the target parameters based on the fitness.

[0088] After updating the target parameters, the predicted value is obtained again using the updated target parameters, and a new fitness is calculated based on the predicted value and the actual value. The steps of calculating fitness and updating target parameters are iteratively executed until the number of iterations reaches the preset number of iterations.

[0089] A5. The target parameters updated at the preset number of iterations shall be used as the target parameters for solving the problem.

[0090] When the number of iterations is reached, the computing platform uses the set of target parameters returned by the heuristic algorithm as the optimal target parameters, that is, the target parameters to be solved.

[0091] S203. Substitute the solved target parameters into the second-order parameter model to obtain the modeled second-order parameters.

[0092] The computing platform inputs the solved target parameters into the second-order parameter model and outputs the completed second-order parameters. In subsequent use, the specific values ​​of the second-order parameters can be calculated by inputting the currently obtained state of charge (SOC), temperature (T), and discharge rate into the completed second-order parameter model.

[0093] In summary, the battery internal resistance parameter modeling method provided in this application, when modeling second-order parameters, transforms the tabular estimation problem of mutually independent second-order parameters under different temperatures T and states of charge (SOC) into a surface parameter estimation problem based on the established second-order parameter model. This strengthens the correlation between different SOCs, temperatures T, and second-order model parameters, reduces the number of parameters to be optimized, significantly accelerates model convergence, and enables rapid modeling of second-order parameters, reducing the difficulty of obtaining second-order parameters and improving acquisition efficiency.

[0094] Based on the above embodiments, when completing the second-order parameter modeling, in order to improve the applicability and repeatability of the second-order parameter model, the computing platform can also evaluate the second-order parameter model.

[0095] Figure 3 is a schematic flowchart of an embodiment of this application for evaluating a second-order parametric model, as shown in Figure 3, including:

[0096] S301, consistency evaluation is performed on the modeled second-order parameter.

[0097] In some embodiments, the computing platform can use a consistency correlation coefficient to perform consistency evaluation on the modeled second-order parameter.

[0098] For example, the second-order parameter is modeled at least twice to obtain at least two sets of target parameters; the real values of battery temperature and state of charge under different working conditions are input into the second-order parameter model under at least two sets of target parameters respectively to obtain at least two sets of parameter sets of the second-order parameter; and consistency evaluation is performed on the second-order parameter according to the consistency correlation coefficient between the at least two sets of parameter sets.

[0099] Since the value of the second-order parameter is constant at a large current ratio, consistency evaluation of the second-order parameter can be performed only on the second-order parameter at a small current ratio.

[0100] For example, two modeling is performed, and two second-order parameter models can be obtained under the two sets of target parameters. For any second-order parameter model, the real values of battery temperature and obtained SOC in the data set are input into the second-order parameter model, and a set of R2 (parameter set) and a set of τ2 can be obtained. That is, for two second-order parameter models, two sets of R2 and two sets of τ2 can be obtained.

[0101] The consistency correlation coefficient between the two sets of R2 is calculated as and the consistency correlation coefficient between the two sets of τ2 is calculated as

[0102] For example, the consistency correlation coefficient ρ c satisfies the following formula:

[0103] where σ x and σ y are the variances of the two sets of data, μ x and μ y are the means of the two sets of data.

[0104] When the computing platform obtains the consistency correlation between the two sets of R2 and the two sets of τ2, if any of the consistency correlation coefficients is greater than a preset threshold, it is determined that the consistency evaluation fails, and if both of the consistency correlation coefficients are greater than the preset threshold, it is determined that the consistency evaluation passes.

[0105] For example, and it is determined that the consistency evaluation passes, and there are or determining that the consistency evaluation fails.

[0106] S302, if the consistency evaluation fails, adjusting the data set and / or the preset parameter range, and re-modeling the second-order parameter.

[0107] If the consistency evaluation fails, it can be determined that there may be abnormalities in the obtained data set, or the preset parameter range of the target parameter may be abnormal, and the data set and / or the preset parameter range need to be adjusted, and the process of modeling the second-order parameter in the above embodiment is re-executed based on the adjusted data set and / or the preset parameter range.

[0108] For example, when the computing platform determines that the consistency evaluation fails, it can output a failure prompt information, which is used to instruct the user to adjust the data set and / or the preset parameter range, and when the computing platform re-receives the instruction of modeling the second-order parameter, the computing platform re-executes the process of modeling the second-order parameter.

[0109] S303, if the consistency evaluation passes, outputting the modeled second-order parameter.

[0110] When it is determined that the consistency evaluation passes, the computing platform can output any set of second-order parameters as the modeled second-order parameter.

[0111] FIG. 4 is a schematic diagram of SOC estimation based on the second-order parameter modeled by the embodiment of the application. As shown in FIG. 4, when the second-order parameter modeled by the method of the application is used for SOC estimation, the predicted value of SOC is close to the actual value, and the error is very small.

[0112] In the above method, by performing consistency evaluation on the modeled second-order parameter, the generalization and repeatability of the second-order parameter model can be effectively improved, the deviation caused by abnormal data can be reduced, and the probability of affecting the accuracy of subsequent SOC estimation can be reduced.

[0113] On the basis of the above embodiment, the embodiment of the application further provides a battery internal resistance parameter modeling device.

[0114] FIG. 5 is a structural schematic diagram of a battery internal resistance parameter modeling device 50 provided by the embodiment of the application. As shown in FIG. 5, it comprises:

[0115] A first acquisition module 501 is configured to acquire a data set for parameter modeling and a second-order parameter model corresponding to a second-order parameter in a second-order equivalent circuit model; the data set comprises the temperature, voltage and current of the battery under different working conditions, the second-order equivalent circuit model is used to simulate the working condition of the battery, and the second-order parameter is the internal resistance parameter of the battery.

[0116] The second obtaining module 502 is configured to obtain a true value of the state of charge of the battery according to the data set.

[0117] The modeling module 503 is configured to model the second-order parameter according to the data set, the second-order parameter model, and the true value.

[0118] In some embodiments, the modeling module 503 is further configured to obtain the true value of the state of charge of the battery according to the data set; solve a target parameter in the second-order parameter model according to the data set, the second-order parameter model, and the true value; the target parameter is a parameter in the second-order parameter model other than the second-order parameter; and bring the solved target parameter into the second-order parameter model to obtain the modeled second-order parameter.

[0119] In some embodiments, the modeling module 503 is further configured to obtain a preset parameter range corresponding to the target parameter; obtain a predicted value of the state of charge of the battery according to the preset parameter range, the data set, and the second-order parameter model; determine a fitness of the state of charge according to the predicted value and the true value; update the target parameter according to the fitness, and iteratively perform the step of determining the fitness of the state of charge until a preset iteration number is reached; and take the target parameter updated at the preset iteration number as the solved target parameter.

[0120] In some embodiments, the modeling module 503 is further configured to randomly determine a value of the target parameter from the preset parameter range; bring the determined value of the target parameter into the second-order parameter model to obtain the second-order parameter; and bring the second-order parameter and the data set into the estimation model to obtain the predicted value of the state of charge.

[0121] In some embodiments, the battery internal resistance parameter modeling device 50 further comprises an evaluation module 504.

[0122] The evaluation module 504 is configured to perform consistency evaluation on the modeled second-order parameter; if the consistency evaluation fails, adjust the data set and / or the preset parameter range, and re-model the second-order parameter.

[0123] In some embodiments, the evaluation module 504 is further configured to model the second-order parameter at least twice to obtain at least two groups of the target parameter; input the true values of the battery temperature and the state of charge under different working conditions into the second-order parameter model under the at least two groups of the target parameter respectively to obtain at least two groups of parameter sets of the second-order parameter; and perform consistency evaluation on the second-order parameter according to a consistency correlation coefficient between the at least two groups of parameter sets.

[0124] In some embodiments, the evaluation module 504 is further configured to obtain a consistency correlation coefficient between any two groups of parameter sets, and determine that the consistency evaluation fails if any of the consistency correlation coefficients is greater than a preset threshold.

[0125] The battery internal resistance parameter modeling apparatus provided by the embodiments of the present application can execute the battery internal resistance parameter modeling method provided by any of the embodiments described above, and has similar principles and technical effects, which will not be described here again.

[0126] The embodiments of the present application further provide an electronic device.

[0127] FIG. 6 is a structural schematic diagram of an electronic device 60 provided by the embodiments of the present application, as shown in FIG. 6, which includes:

[0128] The processor 601.

[0129] The memory 602 is configured to store executable instructions of the terminal device.

[0130] Specifically, the program can include program code, and the program code includes computer operation instructions. The memory 602 can include a high-speed RAM memory, and can also include a non-volatile memory such as at least one disk memory.

[0131] The processor 601 is configured to execute the computer operation instructions stored in the memory 602, so as to implement the technical solutions of the battery internal resistance parameter modeling method embodiments described in the foregoing method embodiments.

[0132] The processor 601 can be a central processing unit (CPU) or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0133] Optionally, the electronic device 60 can further include a communication interface 603, so that the communication interface 603 can communicate with an external device, for example, a user terminal (such as a mobile phone or a tablet). In a specific implementation, if the communication interface 603, the memory 602 and the processor 601 are independently implemented, the communication interface 603, the memory 602 and the processor 601 can be connected to each other through a bus and complete communication between each other.

[0134] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc., but it does not mean that there is only one bus or one type of bus.

[0135] Optionally, in a specific implementation, if the communication interface 603, the memory 602, and the processor 601 are integrated on a chip to be implemented, the communication interface 603, the memory 602, and the processor 601 can complete communication through an internal interface.

[0136] The application embodiment also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the technical solution of the battery resistance parameter modeling method embodiment, and the implementation principle and technical effects are similar, and details are not repeated here.

[0137] In a possible implementation, the computer readable medium can include a Random Access Memory (RAM), a Read-Only Memory (ROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk storage or other magnetic storage device, or any other medium that is targeted to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer. Moreover, any connection is properly referred to as a computer readable medium. For example, if software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technology (such as infrared, radio, and microwave), then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technology (such as infrared, radio, and microwave) is included in the definition of medium. As used herein, a disk and a disc include a compact disc, a laser disc, an optical disc, a digital versatile disc (DVD), a floppy disk, and a Blu-ray disc, where a disk usually magnetically reproduces data, and a disc optically reproduces data using a laser. Combinations of the above should also be included in the scope of computer readable medium.

[0138] The embodiment of the application further provides a computer program product comprising a computer program which, when executed by a processor, implements the technical solution of the battery internal resistance parameter modeling method embodiment, and has similar implementation principles and technical effects, which will not be described herein.

[0139] In the specific implementation of the terminal device or the server, it should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in combination with the embodiments of the application can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor.

[0140] Those skilled in the art can understand that all or part of the steps of any of the method embodiments described above can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer readable storage medium, and when the program is executed, all or part of the steps of the method embodiments are executed.

[0141] If the technical solutions of the application are realized in the form of software and sold or used as products, they can be stored in a computer readable storage medium. Based on this understanding, all or part of the technical solutions of the application can be embodied in the form of a software product, which is stored in a storage medium and includes computer programs or a number of instructions. The computer software product makes a computer device (which can be a personal computer, a server, a network device or similar electronic equipment) execute all or part of the steps of the method described in the embodiments of the application.

[0142] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a combination of a series of actions, but those skilled in the art should know that the application is not limited to the order of the actions described, because according to the application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to optional embodiments, and the actions and modules involved are not necessarily required by the application.

[0143] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0144] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0145] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0146] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0147] If the integrated units / modules are implemented in the form of software program modules and sold or used as independent products, they can be stored in a computer readable memory. Based on this understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0148] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0149] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that: they can still modify the technical solutions recorded in the above embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A battery internal resistance parameter modeling method, characterized in that, The method comprises: obtaining a data set for parameter modeling, and a second-order parameter model corresponding to a second-order parameter in a second-order equivalent circuit model; the data set comprises temperature, voltage and current of the battery under different working conditions, the second-order equivalent circuit model is used to simulate the working condition of the battery, and the second-order parameter is a resistance parameter of the battery; modeling the second-order parameter according to the data set and the second-order parameter model.

2. The method of claim 1, wherein, The step of modeling the second-order parameter according to the data set and the second-order parameter model comprises: obtaining a true value of the state of charge of the battery according to the data set; solving a target parameter in the second-order parameter model according to the data set, the second-order parameter model and the true value; the target parameter is a parameter in the second-order parameter model other than the second-order parameter; obtaining the modeled second-order parameter by bringing the solved target parameter into the second-order parameter model.

3. The method of claim 2, wherein, The step of solving the target parameter in the second-order parameter model according to the data set, the second-order parameter model and the true value comprises: obtaining a preset parameter range corresponding to the target parameter; obtaining a predicted value of the state of charge of the battery according to the preset parameter range, the data set and the second-order parameter model; determining the fitness of the state of charge according to the predicted value and the true value; updating the target parameter according to the fitness, and iteratively performing the step of determining the fitness of the state of charge until a preset iteration number is reached; taking the target parameter updated at the preset iteration number as the solved target parameter.

4. The method of claim 3, wherein, The step of obtaining the predicted value of the state of charge of the battery according to the preset parameter range, the data set and the second-order parameter model comprises: randomly determining a value of the target parameter from the preset parameter range; obtaining the second-order parameter by bringing the determined value of the target parameter into the second-order parameter model; obtaining the predicted value of the state of charge by bringing the second-order parameter and the data set into an estimation model.

5. The method according to any one of claims 1 to 4, characterized in that, The method further comprises: evaluating the consistency of the modeled second-order parameter; if the consistency evaluation fails, adjusting the data set and / or the preset parameter range, and re-modeling the second-order parameter.

6. The method of claim 5, wherein, The step of evaluating the consistency of the modeled second-order parameter comprises: modeling the second-order parameter at least twice to obtain at least two groups of target parameters; respectively inputting the true values of the battery temperature and the state of charge under different working conditions into the second-order parameter model under the at least two groups of target parameters to obtain at least two groups of parameter sets of the second-order parameter; evaluating the consistency of the second-order parameter according to the consistency correlation coefficients between the at least two groups of parameter sets.

7. The method of claim 6, wherein, The step of evaluating the consistency of the second-order parameter according to the consistency correlation coefficients between the first parameter set and the second parameter set comprises: obtaining consistency correlation coefficients between any two groups of parameter sets; if any of the consistency correlation coefficients is greater than a preset threshold, it is determined that the consistency evaluation fails.

8. The method of any one of claims 1-4, comprising: When the current rate C is less than a preset value, the second-order parameter model is as follows: When the current rate is greater than or equal to the preset value, the second-order parameter model is as follows: where R2, τ2 are second order parameters, C is the current rate, a1-a 33 are target parameters to be optimized in the second order parameter model, T is the temperature, T0 is the reference temperature, and SOC is the state of charge.

9. A battery internal resistance parameter modeling apparatus, characterized by, a first obtaining module configured to obtain a data set for parameter modeling and a second-order parameter model corresponding to a second-order parameter in a second-order equivalent circuit model; the data set comprising temperature, voltage, and current of the battery under different operating conditions, the second-order equivalent circuit model being configured to simulate an operating condition of the battery, and the second-order parameter being a resistance parameter of the battery; a second obtaining module configured to obtain a true value of a state of charge of the battery according to the data set; a modeling module configured to model the second-order parameter according to the data set, the second-order parameter model, and the true value. comprising:

10. An electronic device, comprising: a memory configured to store a computer program; a processor configured to execute the computer program to implement the method of any one of claims 1-8. a computer program product having stored thereon a computer program, the computer program being executable by a processor to implement the method of any one of claims 1-8.

11. A computer readable storage medium, characterized in that, a computer program product having stored thereon a computer program, the computer program being executable by a processor to implement the method of any one of claims 1-8.

12. A computer program product, characterised in that, a computer program product having stored thereon a computer program, the computer program being executable by a processor to implement the method of any one of claims 1-8.

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