Battery internal resistance parameter modeling method and related device

Abstract

The embodiment of the invention provides a battery internal resistance parameter modeling method and a related device, and relates to the technical field of batteries, and the method comprises the steps: 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 the temperature, the voltage and the current of the battery under different working conditions, the second-order equivalent circuit model is used for simulating the operation working condition of the battery, and the second-order parameter is the internal resistance parameter of the battery; and modeling the second-order parameters according to the data set and the second-order parameter model. According to the method, the table estimation problem of the second-order parameters is converted into the curved surface parameter estimation problem through the established second-order parameter model, the modeling of the second-order parameters can be quickly realized, the difficulty of obtaining the second-order parameters is reduced, and the efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a battery internal resistance parameter modeling method and related devices. Background Art

[0002] Lithium batteries can be viewed as an electrochemical system consisting of electrodes, electrolytes, and separators. In order to simplify the complexity of electrochemical reactions, lithium batteries can be abstracted into an equivalent circuit model to better understand their electrical performance.

[0003] The second-order equivalent circuit model is one of the most widely used equivalent circuit models for lithium batteries. In the process of using the second-order equivalent circuit model, it is necessary to describe the second-order parameter R 2 , τ 2 However, under different battery conditions, the second-order parameter R 2 , τ 2 With high nonlinearity, how to quickly and accurately obtain the second-order parameter R 2 , τ 2 It has become a problem that technical personnel in this field need to solve urgently. Summary of the invention

[0004] The embodiments of the present application provide a battery internal resistance parameter modeling method and related devices, which can realize the rapid acquisition of second-order parameters.

[0005] In a first aspect, an embodiment of the present application provides a battery internal resistance parameter modeling method, comprising:

[0006] Acquire a data set for parameter modeling, and a second-order parameter model corresponding to the second-order parameter in the second-order equivalent circuit model; the data set includes the temperature, voltage and current of the battery under different operating conditions, the second-order equivalent circuit model is used to simulate the operating condition of the battery, and the second-order parameter is the internal resistance parameter of the battery;

[0007] The second-order parameters are modeled according to the data set and the second-order parameter model.

[0008] In some embodiments, modeling the second-order parameter according to the data set and the second-order parameter model includes:

[0009] Acquire a true value of the state of charge of the battery according to the data set;

[0010] 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 is a parameter in the second-order parameter model other than the second-order parameter;

[0011] The solved target parameters are brought into the second-order parameter model to obtain the modeled second-order parameters.

[0012] In some embodiments, solving the target parameter in the second-order parameter model according to the data set, the second-order parameter model, and the true value includes:

[0013] Obtaining a preset parameter range corresponding to the target parameter;

[0014] 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;

[0015] Determining the fitness of the state of charge according to the predicted value and the true value;

[0016] updating the target parameter according to the fitness, and iteratively executing the step of determining the fitness of the state of charge until a preset number of iterations is reached;

[0017] The target parameter updated corresponding to the preset number of iterations is used as the target parameter to be solved.

[0018] In some embodiments, 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 includes:

[0019] Randomly determine the value of the target parameter from the preset parameter range;

[0020] Substituting the determined value of the target parameter into the second-order parameter model to obtain the second-order parameter;

[0021] The second-order parameters and the data set are brought into an estimation model to obtain a predicted value of the state of charge.

[0022] In some embodiments, the method further comprises:

[0023] Conducting consistency evaluation on the second-order parameters of the modeling;

[0024] If the consistency evaluation fails, the data set and / or the preset parameter range are adjusted, and the second-order parameters are remodeled.

[0025] In some embodiments, the consistency evaluation of the modeled second-order parameters includes:

[0026] Modeling the second-order parameters at least twice to obtain at least two groups of target parameters;

[0027] Under at least two sets of the target parameters, respectively, the actual values ​​of the battery temperature and the state of charge under different operating conditions are input into the second-order parameter model to obtain at least two sets of parameter sets of the second-order parameters;

[0028] The consistency of the second-order parameters is evaluated according to the consistency correlation coefficient between the at least two groups of parameter sets.

[0029] In some embodiments, the performing consistency evaluation on the second-order parameters according to the consistency correlation coefficient between the first parameter set and the second parameter set includes:

[0030] Get the consistency correlation coefficient between any two sets of parameter sets;

[0031] If any of the consistency correlation coefficients is greater than a preset threshold, it is determined that the consistency evaluation has failed.

[0032] In a second aspect, an embodiment of the present application provides a cell internal resistance parameter modeling device, comprising:

[0033] A first acquisition module is used 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 includes a temperature, a voltage and a current of a battery under different operating conditions, the second-order equivalent circuit model is used to simulate the operating condition of the battery, and the second-order parameter is an internal resistance parameter of the battery;

[0034] A second acquisition module, used for acquiring a real value of the state of charge of the battery according to the data set;

[0035] A modeling module is used to model the second-order parameter according to the data set, the second-order parameter model, and the true value.

[0036] In a third aspect, the present application provides an electronic device, including: a memory and a processor;

[0037] The memory is used to store computer instructions; the processor is used to execute the computer instructions stored in the memory to implement any method in the first aspect.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement any one of the methods in the first aspect.

[0039] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which implements any one of the methods in the first aspect when executed by a processor.

[0040] The battery internal resistance parameter modeling method and related device provided in the embodiment of the present application are obtained by obtaining a data set for parameter modeling and a second-order parameter model corresponding to the second-order parameter in the 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 tabular estimation problem of the second-order parameter into a surface parameter estimation problem through the established second-order parameter model, which can quickly realize the modeling of the second-order parameter, reduce the difficulty of second-order parameter modeling, and improve the modeling efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of a battery internal resistance parameter modeling method provided in an embodiment of the present application Figure 1 ;

[0042] Figure 2 A schematic diagram of a battery internal resistance parameter modeling method provided in an embodiment of the present application Figure 2 ;

[0043] Figure 3 A flow chart of a consistency evaluation method provided in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of the effect of battery estimation provided by an embodiment of the present application;

[0045] Figure 5 A schematic diagram of the structure of a battery internal resistance parameter modeling device provided in an embodiment of the present application;

[0046] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0048] In the embodiments of the present application, words such as "first" and "second" are used to distinguish the same or similar items with substantially the same functions and effects, and do not limit their order. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0049] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0050] Lithium batteries can be regarded as electrochemical systems composed of electrodes, electrolytes, and separators. In order to simplify the complexity of electrochemical reactions, lithium batteries can be abstracted into an equivalent circuit model to better understand their electrical properties. The second-order equivalent circuit model is a common lithium battery equivalent circuit model. It consists of two capacitors and two resistors, which represent the internal resistance of the battery, electrolyte dissipation, and the capacitance between the battery plates. By modeling the circuit parameters and fitting the voltage-current curve, the equivalent circuit model of the lithium battery can be obtained, and the accuracy of the model can be further improved by adjusting the parameters.

[0051] The second-order equivalent circuit model converts the terminal voltage U t (t) is described as:

[0052]

[0053] Among them, U ocv is the open circuit voltage, I is the circuit current, R 0 Used to describe ohmic polarization, the first-order parameter R 1 , τ 1 Used to describe electrochemical polarization, the second-order parameter R 2 , τ 2 It is used to describe the concentration polarization caused by solid phase diffusion. Among them, both the first-order parameters and the second-order parameters can be called the internal resistance parameters of the battery.

[0054] Since the second-order parameter R 2 , τ 2 It is affected by the combined effects of the state of charge (SOC), temperature T and discharge rate C, and is highly nonlinear.

[0055] In the related art, R is usually determined by looking up the difference in the table. 2 , τ 2In order to ensure the interpolation accuracy, it is necessary to finely divide the table consisting of the three dimensions of state of charge SOC, temperature T and discharge rate C, resulting in a huge number of variables to be optimized. Excessive variables to be optimized make the optimization process easy to fall into local optimality, and it is difficult to ensure the repeatability and rapid convergence of the second-order parameter estimation, resulting in difficulty in second-order parameter modeling and low efficiency.

[0056] In view of this, an embodiment of the present application provides a battery internal resistance parameter modeling method and related devices. By establishing a second-order parameter model, the table estimation problem in the second-order parameter modeling process is converted into a surface parameter estimation problem, which can quickly model the second-order parameters, reduce the difficulty of obtaining the second-order parameters, and improve efficiency.

[0057] The following specific embodiments are used to describe in detail the technical solution of the present application and how the technical solution of the present application solves the above technical problems. The following specific embodiments can be implemented independently or in combination with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0058] Figure 1 A schematic diagram of a battery internal resistance parameter modeling method provided in an embodiment of the present application Figure 1 , the execution subject of the embodiment of the present application may be a computing platform, server, processor, etc. with data processing capabilities. Taking the computing platform as an example, Figure 1 As shown, the battery internal resistance parameter modeling method includes the following steps:

[0059] S101. Acquire a data set for parameter modeling and a second-order parameter model corresponding to second-order parameters 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 cycle simulation experiments of light vehicle driving conditions, light vehicle driving test procedures, and cycle test conditions at temperatures of -20°C, 0°C, 25°C, and 40°C. The temperature, voltage, and current of each cell in the battery pack are recorded at a sampling frequency of more than 1 Hz to obtain the data set.

[0061] Optionally, at least 2 sets of data are tested at the same temperature and temperature to reduce the probability that a sudden change in data will lead to deviations in subsequent estimation results.

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

[0063] When the discharge rate is a small current rate (the discharge rate is less than a preset value), the second-order parameter model can be as follows:

[0064]

[0065] When the discharge rate is a large current rate (the discharge rate is greater than or equal to a preset value), the second-order parameter model can be as follows:

[0066]

[0067] Among them, T 0 is the reference temperature, C is the current rate, and a1-a33 are the parameters to be optimized in the second-order parameter model.

[0068] S102: Modeling the second-order parameters according to the data set and the second-order parameter model.

[0069] In some embodiments, modeling the second-order parameters (also referred to as calibrating the second-order parameters) may refer to solving the parameters to be optimized (referred to as target parameters) in the established second-order parameter model to obtain an expression of the second-order parameter model, that is, determining the function expression corresponding to the second-order parameters.

[0070] When the computing platform obtains the data set and the second-order parameter model, it can use a heuristic algorithm to iteratively solve the target parameters in the second-order parameter model to obtain the target parameters, and then bring the solved target parameters into the second-order parameter model to determine the function expression corresponding to the second-order parameters.

[0071] Combine the following Figure 2 The process of modeling the second-order parameters according to the data set and the second-order parameter model is described, such as Figure 2 As shown, including:

[0072] S201. Acquire a true value of the state of charge of the battery according to the data set.

[0073] In some embodiments, when the computing platform obtains the data set, the ampere-hour integration method can be used to process the data collected at each sampling point to obtain the SOC value corresponding to each sampling point, that is, the true value of the state of charge of the battery. Among them, the ampere-hour integration method is a basic method for battery power measurement, which calculates the SOC by accumulating the integral of the battery current. The specific implementation method of obtaining the true value of the SOC using the ampere-hour integration method can refer to the implementation method in the prior art, which will not be repeated here.

[0074] S202. 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.

[0075] In some embodiments, the computing platform may use a heuristic algorithm to solve the target parameter in the second-order parameter model based on the data set, the second-order parameter model and the true value.

[0076] Exemplarily, solving the target parameters in the second-order parameter model using a heuristic algorithm may include the following steps:

[0077] A1. Obtain a preset parameter range corresponding to the target parameter.

[0078] The preset parameter range may be R estimated based on prior knowledge. 2 , τ 2 The value range of a is estimated on this basis. 1 to a 33 The value range of .

[0079] For example, a 1 to a 33 The value range of can be as follows:

[0080]

[0081]

[0082] 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.

[0083] In some embodiments, the computing platform may obtain the predicted value in the following manner.

[0084] Exemplarily, a heuristic algorithm is selected, and based on the heuristic algorithm, the value of the target parameter is randomly determined from the preset parameter range; the value of the target parameter determined is substituted into the second-order parameter model to obtain the second-order parameter; the second-order parameter and the data set are substituted into the estimation model to obtain the predicted value of the state of charge.

[0085] Taking the heuristic algorithm as the GWO algorithm as an example, the population size is set to M (for example, 30), the number of iterations is set to N (for example, 1000), and the 33 target parameters are randomly initialized in the value range of the above target parameters to generate M groups of initial parameters. Each group of initial parameters includes 33 target parameters.

[0086] For any set of parameters (any population), 33 target parameters are brought into the second-order parameter model to obtain the second-order parameters. The second-order parameters and the data set are brought into a pre-established SOC estimation model to obtain the predicted value of the SOC at each sampling point output by the SOC estimation model. The SOC estimation model may adopt a Kalman filter estimation model.

[0087] A3. Determine the adaptability of the state of charge according to the predicted value and the true value.

[0088] For any population, when the computing platform obtains the predicted value and true value of SOC corresponding to each sampling point, the fitness of SOC can be obtained in the following manner.

[0089]

[0090] Where, soc(i) is the SOC value obtained by ampere-hour integration at the i-th sample point, is the SOC value of the ith sample point estimated by the estimation model under given second-order model parameters, N is the number of samples used for parameter estimation, and the max(·) term represents the maximum value in the absolute value sequence of the error.

[0091] A4. Update the target parameter according to the fitness, and iteratively execute the step of determining the fitness of the state of charge until a preset number of iterations is reached.

[0092] In some embodiments, taking the GWO algorithm as an example, when the computing platform obtains the fitness of the SOC corresponding to each population, the M populations are sorted and classified according to the fitness, and the target parameters are updated according to the sorted and classified populations and the parameter update rules of the GWO algorithm. Among them, the specific implementation method of the update can refer to the update method in the prior art, which will not be repeated here. It should be understood that if other heuristic algorithms (for example, genetic algorithms, simulated annealing algorithms) are used, the method of updating the target parameters according to the fitness adopts the update method corresponding to the algorithm.

[0093] After the target parameter is updated, the predicted value is reacquired using the updated target parameter, and a new fitness is calculated based on the predicted value and the true value, and the steps of calculating the fitness and updating the target parameter are iteratively performed until the number of iterations reaches a preset number of iterations.

[0094] A5. Use the target parameter updated corresponding to the preset number of iterations as the target parameter to be solved.

[0095] When the number of iterations is reached, the computing platform uses the set of target parameters returned by the acquired heuristic algorithm as the optimal target parameters, that is, the target parameters to be solved.

[0096] S203: Bring the solved target parameters into the second-order parameter model to obtain the modeled second-order parameters.

[0097] The computing platform brings the solved target parameters into the second-order parameter model and outputs the second-order parameters that have been modeled. In subsequent use, the currently acquired state of charge SOC, temperature T, and discharge rate can be brought into the modeled second-order parameter model to calculate the specific value of the second-order parameter.

[0098] In summary, the modeling method of battery internal resistance parameters provided in the embodiment of the present application, when modeling the second-order parameters, based on the established second-order parameter model, converts the tabular estimation problem of the second-order parameters that are independent of each other at different temperatures T and states of charge SOC into a surface parameter estimation problem, strengthens the correlation between different SOCs, temperatures T and the second-order model parameters, reduces the amount of parameters to be optimized, greatly accelerates the model convergence speed, can quickly realize the modeling of the second-order parameters, reduce the difficulty of obtaining the second-order parameters, and improve the acquisition efficiency.

[0099] On the basis of the above-mentioned embodiment, when the second-order parameter modeling is completed, in order to improve the scope of application and repeatability of the second-order parameter model, the computing platform may also evaluate the second-order parameter model.

[0100] Figure 3 A schematic diagram of a process for evaluating a second-order parameter model provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, including:

[0101] S301, performing consistency evaluation on the second-order parameters of the modeling.

[0102] In some embodiments, the computing platform may use a consistency correlation coefficient to perform consistency evaluation on the modeled second-order parameters.

[0103] Exemplarily, the second-order parameters are modeled at least twice to obtain at least two groups of target parameters; under the at least two groups of target parameters, the actual values ​​of the battery temperature and state of charge under different operating conditions are respectively input into the second-order parameter model to obtain at least two groups of parameter sets of the second-order parameters; and the consistency of the second-order parameters is evaluated according to the consistency correlation coefficient between the at least two groups of parameter sets.

[0104] Among them, since the value of the second-order parameter is a constant under a large current rate, the consistency evaluation of the second-order parameter can be performed only on the second-order parameter under a small current rate.

[0105] Taking two modelings as an example, under the two sets of target parameters, two second-order parameter models can be obtained. For any second-order parameter model, the battery temperature in the data set and the real value of the acquired SOC are brought into the second-order parameter model to obtain a set of R 2 (parameter set) and a set τ 2 That is, for two second-order parameter models, two sets of R 2 and two groups of τ 2

[0106] Calculate the two groups of R separately 2 The consistency correlation coefficient and two groups of τ 2 The consistency correlation coefficient

[0107] For example, the consistency correlation coefficient ρ c The following formula can be satisfied:

[0108]

[0109] Among them, 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 groups of data.

[0110] The computing platform obtains two sets of R 2 and two groups of τ 2 When the consistency correlation between the two groups is determined, if any of the consistency correlation coefficients is greater than the preset threshold, it is determined that the consistency evaluation has not passed; if both of the consistency correlation coefficients are greater than the preset threshold, it is determined that the consistency evaluation has passed.

[0111] For example, and It is determined that the consistency evaluation has passed, and there is or It is determined that the consistency evaluation has not passed.

[0112] S302: If the consistency evaluation fails, the data set and / or the preset parameter range are adjusted, and the second-order parameters are remodeled.

[0113] If the consistency evaluation fails, it can be determined that there may be anomalies in the acquired data set, or there may be anomalies in the preset parameter range of the target parameter. In this case, it is necessary to adjust the data set and / or the preset parameter range, and based on the adjusted data set and / or the preset parameter range, re-execute the process of modeling the second-order parameters in the above embodiment.

[0114] Exemplarily, when the computing platform determines that the consistency evaluation has failed, it can output a failure prompt message, wherein the failure prompt message is used to instruct the user to adjust the data set and / or the preset parameter range. When the computing platform re-receives an instruction to model the second-order parameters, the computing platform re-executes the process of modeling the second-order parameters.

[0115] S303: If the consistency evaluation is passed, output the second-order parameters of the modeling.

[0116] When it is determined that the consistency evaluation has passed, the computing platform can output any set of second-order parameters as the second-order parameters for modeling.

[0117] Figure 4 Schematic diagram of SOC estimation based on the second-order parameters modeled in the embodiment of the present application, such as Figure 4 It can be seen that when the SOC is estimated based on the second-order parameters modeled by the method of the present application, the predicted value of the SOC is close to the actual value and the error is very small.

[0118] In the above method, by performing consistency evaluation on the modeled second-order parameters, the generalization and repeatability of the second-order parameter model can be effectively improved, and the probability of the accuracy of subsequent SOC estimation being affected by the deviation caused by abnormal data can be reduced.

[0119] Based on the above embodiments, the embodiments of the present application also provide a battery internal resistance parameter modeling device.

[0120] Figure 5 A schematic diagram of the structure of a battery internal resistance parameter modeling device 50 provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, including:

[0121] The first acquisition module 501 is used to acquire a data set for parameter modeling, and a second-order parameter model corresponding to the second-order parameters in the second-order equivalent circuit model; the data set includes the temperature, voltage and current of the battery under different operating conditions, the second-order equivalent circuit model is used to simulate the operating conditions of the battery, and the second-order parameters are the internal resistance parameters of the battery.

[0122] The second acquisition module 502 is used to acquire a real value of the state of charge of the battery according to the data set.

[0123] The modeling module 503 is used to model the second-order parameter according to the data set, the second-order parameter model, and the true value.

[0124] In some embodiments, the modeling module 503 is also used to obtain the true value of the state of charge of the battery based on the data set; solve the target parameters in the second-order parameter model based on the data set, the second-order parameter model, and the true value; the target parameters are the parameters in the second-order parameter model other than the second-order parameters; the solved target parameters are brought into the second-order parameter model to obtain the modeled second-order parameters.

[0125] In some embodiments, the modeling module 503 is also used to obtain a preset parameter range corresponding to the target parameter; obtain a predicted value of the state of charge of the battery based on the preset parameter range, the data set and the second-order parameter model; determine the fitness of the state of charge based on the predicted value and the true value; update the target parameter based on the fitness, and iterate the step of determining the fitness of the state of charge until a preset number of iterations is reached; and use the target parameter updated corresponding to the preset number of iterations as the target parameter to be solved.

[0126] In some embodiments, the modeling module 503 is also used to randomly determine the 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; bring the second-order parameter and the data set into the estimation model to obtain the predicted value of the state of charge.

[0127] In some embodiments, the battery internal resistance parameter modeling device 50 further includes an evaluation module 504 .

[0128] The evaluation module 504 is used to perform consistency evaluation on the modeled second-order parameters; if the consistency evaluation fails, the data set and / or the preset parameter range are adjusted to re-model the second-order parameters.

[0129] In some embodiments, the evaluation module 504 is also used to model the second-order parameters at least twice to obtain at least two sets of target parameters; under at least two sets of target parameters, the actual values ​​of the battery temperature and state of charge under different operating conditions are respectively input into the second-order parameter model to obtain at least two sets of parameter sets of the second-order parameters; and the consistency of the second-order parameters is evaluated according to the consistency correlation coefficient between the at least two sets of parameter sets.

[0130] In some embodiments, the evaluation module 504 is further used to obtain a consistency correlation coefficient 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 has not passed.

[0131] The battery internal resistance parameter modeling device provided in the embodiment of the present application can execute the battery internal resistance parameter modeling method provided in any of the above embodiments, and its principles and technical effects are similar and will not be repeated here.

[0132] An embodiment of the present application also provides an electronic device.

[0133] Figure 6 A schematic diagram of the structure of an electronic device 60 provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, including:

[0134] Processor 601.

[0135] The memory 602 is used to store executable instructions of the terminal device.

[0136] Specifically, the program may include program codes, and the program codes include computer operation instructions. The memory 602 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0137] The processor 601 is used to execute the computer-executable instructions stored in the memory 602 to implement the technical solution of the battery internal resistance parameter modeling method embodiment described in the aforementioned method embodiment.

[0138] The processor 601 may 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.

[0139] Optionally, the electronic device 60 may further include a communication interface 603, so that communication interaction can be performed with an external device through the communication interface 603. The external device may be, for example, a user terminal (e.g., a mobile phone, a tablet). In a specific implementation, if the communication interface 603, the memory 602, and the processor 601 are implemented independently, the communication interface 603, the memory 602, and the processor 601 may be interconnected through a bus and communicate with each other.

[0140] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (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.

[0141] Optionally, in a specific implementation, if the communication interface 603, the memory 602 and the processor 601 are integrated on a chip, the communication interface 603, the memory 602 and the processor 601 can communicate through an internal interface.

[0142] A computer-readable storage medium is also provided in an embodiment of the present application, on which a computer program is stored. When the computer program is executed by a processor, the technical solution of the above-mentioned battery internal resistance parameter modeling method embodiment is implemented. The implementation principle and technical effect are similar and will not be repeated here.

[0143] In one possible implementation, a computer-readable medium may 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 intended to carry or store the required program code in the form of instructions or data structures and can be accessed by a computer. Moreover, any connection is appropriately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server or other remote source using a coaxial cable, a fiber optic cable, a twisted pair, a digital subscriber line (DSL) or wireless technologies such as infrared, radio and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL or wireless technologies such as infrared, radio and microwave are included in the definition of the medium. Disks and optical disks as used herein include optical disks, laser disks, optical disks, digital versatile disks (DVDs), floppy disks and Blu-ray disks, where disks usually reproduce data magnetically, while optical disks reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0144] A computer program product is also provided in an embodiment of the present application, including a computer program. When the computer program is executed by a processor, the technical solution of the above-mentioned battery internal resistance parameter modeling method embodiment is implemented. Its implementation principle and technical effect are similar and will not be repeated here.

[0145] In the specific implementation of the above-mentioned terminal device or server, it should be understood that the processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor.

[0146] Those skilled in the art will appreciate that all or part of the steps of any of the above method embodiments may be completed by hardware associated with program instructions. The aforementioned program may be stored in a computer-readable storage medium, and when the program is executed, all or part of the steps of the above method embodiments are executed.

[0147] If the technical solution of the present application is implemented in the form of software and sold or used as a product, it can be stored in a computer-readable storage medium. Based on such an understanding, all or part of the technical solution of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a computer program or several instructions. The computer software product enables a computer device (which can be a personal computer, a server, a network device, or a similar electronic device) to perform all or part of the steps of the method described in the embodiment of the present application.

[0148] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0149] It should be further noted that, although the various steps in the flowchart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0150] It should be understood that the above-mentioned device embodiments are only illustrative, and the device of the present application can also be implemented in other ways. For example, the division of units / modules in the above-mentioned embodiments is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0151] In addition, unless otherwise specified, each functional unit / module in each embodiment of the present application may be integrated into one unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The above-mentioned integrated unit / module may be implemented in the form of hardware or in the form of a software program module.

[0152] If the integrated unit / module is implemented in the form of hardware, the hardware may be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc. If not specifically stated, the processor may be any appropriate hardware processor, such as a CPU, a GPU, an FPGA, a DSP, an ASIC, etc. If not specifically stated, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc.

[0153] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0154] In the above embodiments, the description of each embodiment has its own emphasis. For the part not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to 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: include: Acquire a data set for parameter modeling, and a second-order parameter model corresponding to the second-order parameter in the second-order equivalent circuit model; the data set includes the temperature, voltage and current of the battery under different operating conditions, the second-order equivalent circuit model is used to simulate the operating condition of the battery, and the second-order parameter is the internal resistance parameter of the battery; The second-order parameters are modeled according to the data set and the second-order parameter model.

2. The method according to claim 1, characterized in that The modeling of the second-order parameter according to the data set and the second-order parameter model comprises: Acquire a true value of the state of charge of the battery according to the data set; 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 is a parameter in the second-order parameter model other than the second-order parameter; The solved target parameters are brought into the second-order parameter model to obtain the modeled second-order parameters.

3. The method according to claim 2, characterized in that The step of solving a target parameter in the second-order parameter model according to the data set, the second-order parameter model, and the true value includes: 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 executing the step of determining the fitness of the state of charge until a preset number of iterations is reached; The target parameter updated corresponding to the preset number of iterations is used as the target parameter to be solved.

4. The method according to claim 3, characterized in that 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 includes: Randomly determine the value of the target parameter from the preset parameter range; Substituting the determined value of the target parameter into the second-order parameter model to obtain the second-order parameter; The second-order parameters and the data set are brought into an estimation model to obtain a predicted value of the state of charge.

5. The method according to any one of claims 2 to 4, characterized in that: The method further comprises: Conducting consistency evaluation on the second-order parameters of the modeling; If the consistency evaluation fails, the data set and / or the preset parameter range are adjusted, and the second-order parameters are remodeled.

6. The method according to claim 5, characterized in that The consistency evaluation of the second-order parameters of the modeling includes: Modeling the second-order parameters at least twice to obtain at least two groups of target parameters; Under at least two sets of the target parameters, respectively, the actual values ​​of the battery temperature and the state of charge under different operating conditions are input into the second-order parameter model to obtain at least two sets of parameter sets of the second-order parameters; The consistency of the second-order parameters is evaluated according to the consistency correlation coefficient between the at least two groups of parameter sets.

7. The method according to claim 6, characterized in that The performing consistency evaluation on the second-order parameters according to the consistency correlation coefficient between the at least two groups of parameter sets comprises: Get the consistency correlation coefficient between any two sets of parameter sets; If any of the consistency correlation coefficients is greater than a preset threshold, it is determined that the consistency evaluation has failed.

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

9. A battery internal resistance parameter modeling device, characterized in that: include: A first acquisition module is used 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 includes a temperature, a voltage and a current of a battery under different operating conditions, the second-order equivalent circuit model is used to simulate the operating condition of the battery, and the second-order parameter is an internal resistance parameter of the battery; A second acquisition module, used for acquiring a real value of the state of charge of the battery according to the data set; A modeling module is used to model the second-order parameter according to the data set, the second-order parameter model, and the true value.

10. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to execute the computer program to implement the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 8.

12. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 8 when executed by a controller.

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