Lithium battery internal resistance identification method, storage medium and electronic device

Abstract

The present invention provides a lithium battery internal resistance identification method, storage medium and electronic device, the lithium battery internal resistance identification method comprising: collecting the current and voltage of the lithium battery according to a preset collection time interval; passing the current and voltage into a created internal resistance identification equivalent circuit model to determine the parameters to be identified of the internal resistance identification equivalent circuit model, and determining the open circuit voltage and internal resistance of the lithium battery according to the parameters to be identified; obtaining the range of each parameter to be identified of the internal resistance identification equivalent circuit model according to the adaptive adjustment memory factor of the internal resistance identification equivalent circuit model; passing the range of each parameter to be identified into an internal resistance distribution model to obtain the total internal resistance value of the lithium battery, and determining the statistical distribution of the total internal resistance of the lithium battery by running the internal resistance distribution model at different times. The present invention can ensure the accuracy of battery internal resistance identification under various working environments, and has good applicability under various working environments.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery parameter identification, and relates to an internal resistance identification method, and in particular to a lithium battery internal resistance identification method, a storage medium and an electronic device. Background Art

[0002] With the use of new energy vehicles or energy storage power stations, the internal resistance of lithium batteries will gradually increase after multiple charge and discharge, and the change in battery internal resistance will affect the degree of aging, heat generation, etc. At the same time, when the battery pack is under the same charging and discharging conditions, if the internal resistance of each single cell is inconsistent, the thermal power loss between different cells will be different, which will affect the service life of the entire battery pack. Therefore, in order to achieve better management of batteries and prevent or avoid the occurrence of corresponding problems, it is necessary to predict and estimate the internal resistance of the battery.

[0003] At present, most existing battery internal resistance identification methods are based on data sets obtained under stable working conditions, which have very high data requirements. If extrapolated to various working environments such as non-cross-flow or frequency modulation, the identification error will increase significantly. Moreover, the difficulty of online identification of battery internal resistance in various working environments is also increased.

[0004] Therefore, how to solve the defects of the prior art, such as the inability to ensure the accuracy of battery internal resistance identification under various working environments, has become a technical problem that needs to be urgently solved by technical personnel in this field. Summary of the invention

[0005] In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a lithium battery internal resistance identification method, storage medium and electronic device to solve the problem that the prior art cannot ensure the accuracy of battery internal resistance identification under various working environments.

[0006] To achieve the above-mentioned purpose and other related purposes, the present invention provides a method for identifying the internal resistance of a lithium battery on one hand, the method comprising: collecting the current and voltage of the lithium battery at a preset collection time interval; passing the current and the voltage into a created internal resistance identification equivalent circuit model to determine the parameters to be identified of the internal resistance identification equivalent circuit model, and determining the open circuit voltage and the internal resistance of the lithium battery according to the parameters to be identified; obtaining the range of each parameter to be identified of the internal resistance identification equivalent circuit model according to the adaptive adjustment memory factor of the internal resistance identification equivalent circuit model; passing the range of each parameter to be identified into an internal resistance distribution model to obtain the total internal resistance value of the lithium battery, and determining the statistical distribution of the total internal resistance of the lithium battery by running the internal resistance distribution model at different times.

[0007] In one embodiment of the present invention, the step of passing the current and the voltage into the created internal resistance identification equivalent circuit model, determining the parameters to be identified of the internal resistance identification equivalent circuit model, and determining the open circuit voltage and internal resistance of the lithium battery according to the parameters to be identified includes: passing the current and the voltage as input quantities into the input matrix of the internal resistance identification equivalent circuit model; using an adaptive adjustment memory factor to complete the iterative calculation of the internal resistance identification equivalent circuit model to determine the open circuit voltage and internal resistance of the lithium battery, wherein the equation for the open circuit voltage is fitted using a polynomial.

[0008] In one embodiment of the present invention, the step of obtaining the range of each parameter to be identified of the internal resistance identification equivalent circuit model based on the adaptive adjustment memory factor of the internal resistance identification equivalent circuit model includes: adjusting the adaptive adjustment memory factor to determine the range of polynomial coefficients of each order in the open circuit voltage polynomial.

[0009] In one embodiment of the present invention, the internal resistance is the total internal resistance, including ohmic internal resistance and polarization internal resistance; the internal resistance distribution model includes a particle swarm model; the step of passing the range of each parameter to be identified into the internal resistance distribution model to obtain the total internal resistance value of the lithium battery includes: using the total resistance and the polynomial coefficient range, and taking the calculated terminal voltage as the objective function; through the particle swarm model, each particle continuously moves and adjusts within the polynomial coefficient range, and the total internal resistance value of the lithium battery is determined by the value of the objective function.

[0010] In one embodiment of the present invention, the value of the objective function is the difference between the terminal voltage and the collected voltage; the step of determining the total internal resistance of the lithium battery by taking the value of the objective function through the particle swarm model, in which each particle continuously moves and adjusts within the range of the polynomial coefficient, includes: each particle continuously moves and adjusts within the range of the polynomial coefficient until the difference between the terminal voltage and the collected voltage is minimized; the position of the particle at this time is taken as its optimal position, the corresponding values ​​of each polynomial coefficient are taken as its optimal solution in this operation, and the value of the coefficient as the internal resistance is taken as the total internal resistance of the lithium battery.

[0011] In one embodiment of the present invention, the step of determining the statistical distribution of the total internal resistance of the lithium battery by running the internal resistance distribution model at different times includes: taking the internal resistance value obtained each time as the horizontal axis and the frequency of occurrence of the internal resistance value identified this time as the vertical axis; the frequency refers to the number of times the internal resistance value appears during identification divided by the total number of identifications; in response to the distribution of the internal resistance values ​​obtained at each identification satisfying the normal distribution, the expected value of the normal distribution is used as the internal resistance value of the battery.

[0012] In one embodiment of the present invention, after the step of determining the statistical distribution of the total internal resistance of the lithium battery, the steps of the lithium battery internal resistance identification method include: as the lithium battery undergoes a charge and discharge cycle, taking the change in the expected value of the normal distribution as the change in the total internal resistance; and analyzing the deterioration trend of the lithium battery through the change in the total internal resistance.

[0013] In one embodiment of the present invention, while collecting the current and voltage of the lithium battery at a preset collection time interval, the lithium battery internal resistance identification method also includes: using a first-order RC equivalent circuit to create the internal resistance identification equivalent circuit model, and the internal resistance identification equivalent circuit model includes a mathematical model of the open circuit voltage and the battery internal resistance.

[0014] To achieve the above-mentioned purpose and other related purposes, the present invention provides a computer-readable storage medium on the other hand, which stores a computer program, and when the computer program is executed by a processor, the method for identifying the internal resistance of a lithium battery is implemented.

[0015] To achieve the above-mentioned purpose and other related purposes, the last aspect of the present invention provides an electronic device, including: a processor and a memory; the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory, so that the electronic device executes the lithium battery internal resistance identification method.

[0016] As described above, the lithium battery internal resistance identification method, storage medium and electronic device of the present invention have the following beneficial effects:

[0017] (1) The present invention can be used in various working environments to solve the problem that the internal resistance of the battery is difficult to accurately identify in various working environments. It has good practical applicability in various working environments, for example, it has good practical applicability in a non-constant current and constant voltage working environment.

[0018] (2) The accuracy of the internal resistance identification of the present invention is good. From the test results, the internal resistance distribution of the battery can be accurately obtained. When the distribution of the internal resistance of the battery satisfies the normal distribution, it can be considered that the expected value of the distribution is the internal resistance value of the battery.

[0019] (3) The method implemented by the present invention occupies very little memory space, only 15kB, and will not affect other functions in actual application. At the same time, the speed is also very fast in actual operation. For a whole day's data of a battery, it only takes about 2 seconds to process it using the method of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a principle flow chart of a lithium battery internal resistance identification method in one embodiment of the present invention.

[0021] Figure 2It is a diagram showing an equivalent circuit model of a lithium battery internal resistance identification method in one embodiment of the present invention.

[0022] Figure 3 Shown is a statistical distribution diagram of internal resistance in an embodiment of the lithium battery internal resistance identification method of the present invention.

[0023] Figure 4 It is a schematic diagram showing the structural connection of an electronic device in one embodiment of the present invention.

[0024] Component number description

[0025] 4 Electronic devices

[0026] 41 Processor

[0027] 42 Memory

[0028] Steps S11 to S14 DETAILED DESCRIPTION

[0029] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0030] It should be noted that the illustrations provided in the following embodiments are only used to illustrate the basic concept of the present invention in a schematic manner, and thus the illustrations only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0031] The lithium battery internal resistance identification method, storage medium and electronic device of the present invention can ensure the accuracy of battery internal resistance identification under various working environments and have good applicability under various working environments.

[0032] The following will be combined Figures 1 to 4 The principles and implementation methods of a lithium battery internal resistance identification method, storage medium and electronic device of this embodiment are described in detail so that those skilled in the art can understand the lithium battery internal resistance identification method, storage medium and electronic device of this embodiment without creative work.

[0033] See also Figure 1 , which is a principle flow chart of a lithium battery internal resistance identification method in one embodiment of the present invention. Figure 1As shown, the lithium battery internal resistance identification method specifically includes the following steps:

[0034] S11, collecting the current and voltage of the lithium battery according to a preset collection time interval.

[0035] Specifically, the battery data of the power station or electric vehicle is collected and uploaded at regular intervals, and the collected data mainly includes battery working time, current, voltage, temperature and SOC (State of Charge), etc. In practical applications, the preset collection time interval can be more than ten seconds or other reasonably set time intervals.

[0036] In one embodiment, at the same time as step S11, the lithium battery internal resistance identification method further includes: using a first-order RC equivalent circuit to create the internal resistance identification equivalent circuit model, the internal resistance identification equivalent circuit model includes a mathematical model of the open circuit voltage and the battery internal resistance.

[0037] See also Figure 2 , which is an equivalent circuit model diagram of the lithium battery internal resistance identification method of the present invention in one embodiment. Figure 2 As shown, using a first-order RC equivalent circuit, the formula for the internal resistance identification equivalent circuit model is as follows:

[0038]

[0039] U l =U ocv -U p -i l R o

[0040] Among them, U l Indicates the battery terminal voltage, U ocv Indicates the open circuit voltage, U p represents the polarization voltage, i l Indicates current.

[0041] Then the transfer function of the equivalent circuit model based on the internal resistance identification is:

[0042]

[0043] Equivalent to

[0044]

[0045] S12, transferring the current and the voltage into the created internal resistance identification equivalent circuit model, determining the parameters to be identified of the internal resistance identification equivalent circuit model, and determining the open circuit voltage and internal resistance of the lithium battery according to the parameters to be identified.

[0046] In one embodiment, S12 specifically includes the following steps:

[0047] (1) The current and the voltage are input as input quantities into the input matrix of the internal resistance identification equivalent circuit model. Specifically, by using bilinear transformation, the transfer function of the internal resistance identification equivalent circuit model is converted into:

[0048] δU l,k =a 1 ×δU l,k-1 +a 2 ×i l,k +a 3 ×i l,k-1

[0049] Among them, a 1 , a 2 , a 3 It is a coefficient related to the model parameters and changes during the parameter identification process. l,k =U l,k -U ocv,k Indicates the difference between the terminal voltage and the open circuit voltage at the kth sampling time. Open circuit voltage U ocv It is associated with SOC and temperature. Because the sampling time interval is very short, 15s, where 15s is only one implementation form of this embodiment, other values ​​that meet the sampling requirements and are reasonably set are also within the scope of protection of the present invention, such as sampling time ≤ 15s. The SOC change and temperature change during adjacent sampling intervals are negligible, so it is considered that within the adjacent sampling time U ocv,k =U ocv,k-1 , then the bilinear transformation formula is converted to

[0050] U l,k =(1-a 1 )×U ocv,k +a 1 ×U l,k-1 +a 2 ×i l,k +a 3 ×i l,k-1

[0051] Then the parameter matrix x to be identified is k and the input variable matrix A k for

[0052] x k =[(1-a 1 ) ocv,k a 1 a 2 a 3 ] T

[0053] A k=[1 U l,k-1 i l,k i l,k-1 ] T

[0054] Among them, U l,k-1 represents the terminal voltage at time k-1, i l,k represents the current at time k, i l,k-1 Represents the current at time k-1, and these input variables are all sampled.

[0055] Then we can get U ocv And the battery internal resistance:

[0056]

[0057]

[0058]

[0059] (2) The memory factor is adjusted adaptively to complete the iterative calculation of the internal resistance identification equivalent circuit model to determine the open circuit voltage and internal resistance of the lithium battery. The open circuit voltage equation is fitted using a polynomial.

[0060] Specifically, the parameter matrix x of the model is calculated iteratively based on the adaptive MFRLS (Memory Factor Recursive Least Squares) method. k To ensure the stability of the results, the adaptive adjustment memory factor λ is added to indicate the degree of memory of the identification results of the previous moment. After setting the initial value of λ, the adaptive adjustment of λ is performed in the iteration process according to the set conditions at each iteration. The value of λ in the adaptive process is always between 0.9 and 1. The specific iterative calculation process is as follows:

[0061]

[0062]

[0063]

[0064] Among them, P k is the state estimation error covariance matrix, K k is the gain of each iteration, and I is the identity matrix.

[0065] S13, obtaining ranges of parameters to be identified of the internal resistance identification equivalent circuit model according to the adaptive adjustment memory factor of the internal resistance identification equivalent circuit model.

[0066] In one embodiment, the adaptive adjustment memory factor is adjusted to determine the range of polynomial coefficients of each order in the open circuit voltage polynomial.

[0067] In another embodiment, when calculating the to-be-identified parameter at different times, different initial values ​​are assigned to the adaptive adjustment memory factor to determine the range of polynomial coefficients of each order in the open circuit voltage polynomial.

[0068] In another embodiment, different initial values ​​are assigned to the adaptive adjustment memory factor when calculating the parameter to be identified at different times, and the adaptive adjustment memory factor is adjusted during the identification process to determine the range of polynomial coefficients of each order in the open circuit voltage polynomial.

[0069] Specifically, the current, voltage, SOC, temperature and other values ​​sampled each time are transmitted to the particle model to realize online parameter identification, and the total internal resistance distribution of the battery is obtained according to the identified parameter results.

[0070] The ohmic internal resistance, polarization internal resistance and total internal resistance are identified, where the total internal resistance includes the ohmic internal resistance and polarization internal resistance. Because when using the particle swarm algorithm, the ohmic internal resistance and polarization internal resistance fluctuate greatly, but the sum of the two is constant, that is, the distribution of the total internal resistance remains unchanged. At the same time, Uocv is obtained, as described in step S12 (2), and the SOC-OCV curve is fitted using the following polynomial:

[0071]

[0072] Where n is the order of the polynomial, b n is the coefficient of the nth order polynomial, b n To be solved. During identification, λ takes different initial values ​​and the U obtained by each identification is ocv Substitute the values ​​into the above formula to find the coefficients of the polynomial and finally determine the range of each coefficient.

[0073] In practical applications, for each coefficient, such as b 1 By adjusting the adaptive adjustment memory factor, different results can be identified and these results are arranged in order from small to large, then b 1 The coefficient ranges between a minimum and a maximum value.

[0074] S14, passing the range of each of the parameters to be identified into the internal resistance distribution model to obtain the total internal resistance value of the lithium battery, and determining the statistical distribution of the total internal resistance of the lithium battery by running the internal resistance distribution model at different times.

[0075] In one embodiment, the internal resistance is a total internal resistance, including an ohmic internal resistance and a polarization internal resistance; the internal resistance distribution model includes a particle swarm model; the step of transferring the range of each parameter to be identified into the internal resistance distribution model to obtain the total internal resistance value of the lithium battery is defined as step A of S14, including:

[0076] (A1) Using the total resistance and the polynomial coefficient range, the calculated terminal voltage is used as the objective function.

[0077] Specifically, a PSO (Particle Swarm Optimization) algorithm is used to estimate the statistical distribution of the battery internal resistance.

[0078] Among them, the PSO algorithm searches for the optimal solution through collaboration and information sharing between individuals in the group. The PSO algorithm requires each particle to maintain two vectors, a velocity vector and a position vector, during the optimization process. i represents the number of a particle in the total number of particles, and m represents the number of parameters to be solved. The velocity of the particle determines the direction and speed of its movement, while the position reflects the position of the solution represented by the particle in the solution space, which is the basis for evaluating the quality of the solution. The algorithm also requires each particle to maintain its own historical optimal position pBest and the group to maintain a global optimal gBest.

[0079] The speed and position of the i-th particle at the m-th parameter are updated according to the following formulas:

[0080]

[0081]

[0082]

[0083] Among them, c1 and c2 are individual learning factors and social learning factors, which keep the particles inertia, make them have the tendency to expand the search space, and have the ability to explore new areas. It is recommended that the values ​​of c1 and c2 be between 1.5 and 2. ω is the inertia factor, which changes linearly during the PSO search process.

[0084] The calculated terminal voltage is used as the objective function, and the terminal voltage calculation formula is:

[0085]

[0086] Where R is the total internal resistance of the battery. 0 , k 1 , k 2 , …, k 7 The upper and lower limits of b are calculated by n The scope is determined.

[0087] (A2) Through the particle swarm model, each particle continuously moves and adjusts within the range of the polynomial coefficients, and the total internal resistance of the lithium battery is determined by the value of the objective function. In practical applications, the range of each parameter in the particle algorithm is calculated by the recursive least squares method.

[0088] Specifically, combined with the terminal voltage calculation formula, adjust the number of particles, number of iterations, c 1 and c 2 And run it multiple times to finally get the statistical distribution of R.

[0089] In one embodiment, the value of the objective function is the difference between the terminal voltage and the collected voltage. Step (A2) of S14 includes the following steps:

[0090] (A2.1) Each particle continuously moves and adjusts within the range of the polynomial coefficients until the difference between the terminal voltage and the collected voltage is minimized.

[0091] In practical applications, the error between the terminal voltage identified and the sampled voltage can be compared in combination with the voltage curve. When the result of the terminal voltage identification is very close to the sampled battery voltage and the two curves almost overlap, it means that the difference between the terminal voltage and the sampled voltage is the smallest, and the internal resistance identification method is reliable.

[0092] (A2.2) The position of the particle at this time is taken as its optimal position, the corresponding values ​​of the polynomial coefficients are taken as its optimal solution in this calculation, and the value of the coefficient as the internal resistance is taken as the total internal resistance value of the lithium battery.

[0093] In one embodiment, the step of determining the statistical distribution of the total internal resistance of the lithium battery by running the internal resistance distribution model at different times is defined as step B of S14, including:

[0094] (B1) The internal resistance value obtained in each identification is taken as the horizontal axis, and the frequency of occurrence of the internal resistance value identified in that identification is taken as the vertical axis; the frequency refers to the number of times the internal resistance value appears during identification divided by the total number of identifications.

[0095] In practical applications, the identified internal resistance values ​​are managed in a list to form the battery internal resistance test and identification result table in Table 1. It can be seen that as the battery is used, the internal resistance gradually increases. In order to measure the accuracy of the online identification of the internal resistance of the battery, the hybrid power pulse characteristic experiment (Hybrid Pulse Power Characteristic, HPPC) is used to test the internal resistance of the battery and compare it with the internal resistance value obtained by identification. The specific results are shown in Table 1. Because the power station basically operates at a charge and discharge rate of 0.25C during operation, the HPPC results of 0.25C are taken for comparison. It can be seen from Table 1 that after the battery has been used for 18 months, the total internal resistance of the battery has increased by 24%. It can be seen that when λ takes different initial values, the results of the internal resistance identification are different, which shows that it is necessary to obtain the statistical distribution of the battery internal resistance.

[0096] Table 1 Battery internal resistance test and identification results

[0097]

[0098] See also Figure 3 , which is a statistical distribution diagram of the internal resistance of a lithium battery in an embodiment of the present invention. Figure 3 As shown in the figure, the statistical distribution of internal resistance obtained by the PSO algorithm presents the internal resistance distribution diagram of the battery after 18 months of use. The horizontal axis in the figure is the internal resistance value obtained by each identification, and the vertical axis is the frequency of occurrence of the value, that is, the number of times it appears during identification divided by the total number of identifications.

[0099] (B2) In response to the distribution of the internal resistance values ​​obtained by each identification satisfying the normal distribution, an expected value of the normal distribution is used as the internal resistance value of the battery.

[0100] Depend on Figure 3 It can be seen that although the internal resistance obtained each time is slightly different, the distribution of the battery internal resistance satisfies the normal distribution. The internal resistance always fluctuates around the expected value of its distribution, and the standard deviation of the fluctuation is less than 0.1. When the distribution of the battery internal resistance satisfies the normal distribution, it can be considered that the mean of the normal distribution is the internal resistance value of the battery.

[0101] In one embodiment, after step B of S14, the steps of the lithium battery internal resistance identification method include:

[0102] As the lithium battery undergoes a charge and discharge cycle, a change in the expected value of the normal distribution is taken as a change in the total internal resistance; and the deterioration trend of the lithium battery is analyzed through the change in the total internal resistance.

[0103] Specifically, for example, in the first cycle, the normal distribution of the battery internal resistance calculated multiple times obeys N(1.3, 0.05), and in the 100th cycle, the internal resistance obeys N(1.44, 0.07). The expected change is (1.44-1.3) / 1.3=0.107, indicating that the internal resistance has increased by 10% on average.

[0104] The protection scope of the lithium battery internal resistance identification method described in the present invention is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present invention are included in the protection scope of the present invention.

[0105] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for identifying the internal resistance of a lithium battery is implemented.

[0106] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned computer-readable storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various computer storage media that can store program codes.

[0107] See also Figure 4 , which is a schematic diagram showing the structural connection of an electronic device in one embodiment of the present invention. Figure 4 As shown, this embodiment provides an electronic device 4, specifically comprising: a processor 41 and a memory 42; the memory 42 is used to store computer programs, and the processor 41 is used to execute the computer programs stored in the memory 42, so that the electronic device 4 executes each step of the lithium battery internal resistance identification method.

[0108] The above-mentioned processor 41 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0109] The memory 42 may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0110] In practical applications, the electronic device may be a computer including components such as a memory, a storage controller, one or more processing units (CPU), a peripheral interface, an RF circuit, an audio circuit, a speaker, a microphone, an input / output (I / O) subsystem, a display screen, other output or control devices, and an external port; the computer includes but is not limited to personal computers such as desktop computers, laptop computers, tablet computers, smart phones, and personal digital assistants (PDAs). In other embodiments, the electronic device may also be a server, which may be arranged on one or more physical servers according to various factors such as function and load, or may be a cloud server composed of a distributed or centralized server cluster, which is not limited in this embodiment.

[0111] In summary, the lithium battery internal resistance identification method, storage medium and electronic device described in the present invention can be used in various working environments to solve the problem that the internal resistance of the battery is difficult to accurately identify in various working environments. It has good practical applicability in various working environments, for example, it has good practical applicability in a non-constant current and constant voltage working environment. The accuracy of the internal resistance identification of the present invention is good. From the test results, the internal resistance distribution of the battery can be accurately obtained. When the distribution of the internal resistance of the battery satisfies the normal distribution, it can be considered that the expected value of the distribution is the internal resistance value of the battery. The method executed by the present invention occupies a very small memory space, only 15kB, and will not affect other functions in actual application. At the same time, the speed is also very fast during actual operation. For the data of a battery for a whole day, it only takes about 2s to process using the method of the present invention. The present invention effectively overcomes the various shortcomings in the prior art and has a high industrial utilization value.

[0112] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for identifying the internal resistance of a lithium battery. It is characterized in that The lithium battery internal resistance identification method comprises: Collect the current and voltage of the lithium battery according to the preset collection time interval; The current and the voltage are transmitted into the created internal resistance identification equivalent circuit model, the parameters to be identified of the internal resistance identification equivalent circuit model are determined, and the open circuit voltage and the internal resistance of the lithium battery are determined according to the parameters to be identified; the internal resistance identification equivalent circuit model is expressed as: IN l =U ocv -IN p -and l R o ; Among them, U l Indicates the battery terminal voltage, U ocv Indicates the open circuit voltage, U p represents the polarization voltage, i l Indicates current; The transfer function based on the internal resistance identification equivalent circuit model is expressed as: Based on the adaptive MFRLS iterative calculation model, the range of each parameter to be identified of the internal resistance identification equivalent circuit model is obtained according to the adaptive adjustment memory factor of the internal resistance identification equivalent circuit model; The range of each parameter to be identified is passed into the internal resistance distribution model to obtain the total internal resistance value of the lithium battery, and the statistical distribution of the total internal resistance of the lithium battery is determined by running the internal resistance distribution model at different times; The internal resistance distribution model includes a particle swarm model; the step of transferring the range of each parameter to be identified into the internal resistance distribution model to obtain the total internal resistance value of the lithium battery includes: Obtaining the objective function using the total resistance and the polynomial coefficient range; Through the particle swarm model, each particle is continuously moved and adjusted within the range of the polynomial coefficient, and the total internal resistance of the lithium battery is determined by the value of the objective function; The step of determining the statistical distribution of the total internal resistance of the lithium battery by running the internal resistance distribution model at different times includes: The internal resistance value obtained at each identification is taken as the horizontal axis, and the frequency of occurrence of the internal resistance value identified at that time is taken as the vertical axis; the frequency refers to the number of times the internal resistance value appears during identification divided by the total number of identifications; In response to the distribution of the internal resistance values ​​obtained through each identification satisfying the normal distribution, the expected value of the normal distribution is used as the internal resistance value of the battery.

2. The method for identifying internal resistance of a lithium battery according to claim 1, It is characterized in that The step of transferring the current and the voltage into the created internal resistance identification equivalent circuit model, determining the parameters to be identified of the internal resistance identification equivalent circuit model, and determining the open circuit voltage and internal resistance of the lithium battery according to the parameters to be identified includes: Passing the current and the voltage as input quantities into an input matrix of the internal resistance identification equivalent circuit model; The memory factor is adjusted adaptively to complete the iterative calculation of the internal resistance identification equivalent circuit model, and the open circuit voltage and internal resistance of the lithium battery are determined. The equation of the open circuit voltage is fitted by a polynomial.

3. The method for identifying internal resistance of a lithium battery according to claim 2, It is characterized in that The step of obtaining the range of each parameter to be identified of the internal resistance identification equivalent circuit model according to the adaptive adjustment memory factor of the internal resistance identification equivalent circuit model comprises: The adaptive adjustment memory factor is adjusted to determine the range of polynomial coefficients of each order in the open circuit voltage polynomial.

4. The method for identifying internal resistance of a lithium battery according to claim 3, It is characterized in that The internal resistance is the total internal resistance, including the ohmic internal resistance and the polarization internal resistance.

5. The method for identifying internal resistance of a lithium battery according to claim 4, It is characterized in that The value of the objective function is the difference between the terminal voltage and the collected voltage; the step of using the particle swarm model to continuously move and adjust each particle within the range of the polynomial coefficient, and determining the total internal resistance of the lithium battery by the value of the objective function includes: Each particle continuously moves and adjusts within the range of the polynomial coefficient until the difference between the terminal voltage and the collected voltage is minimized; The position of the particle at this time is taken as its optimal position, the corresponding values ​​of the polynomial coefficients are taken as the optimal solution in this operation, and the value of the coefficient as the internal resistance is taken as the total internal resistance value of the lithium battery.

6. The method for identifying internal resistance of a lithium battery according to claim 1, It is characterized in that After the step of determining the statistical distribution of the total internal resistance of the lithium battery, the steps of the lithium battery internal resistance identification method include: As the lithium battery undergoes a charge and discharge cycle, a change in the expected value of the normal distribution is used as a change in the total internal resistance; The deterioration trend of the lithium battery is analyzed through the change of the total internal resistance.

7. The method for identifying internal resistance of a lithium battery according to claim 1, It is characterized in that While collecting the current and voltage of the lithium battery at the preset collection time interval, the lithium battery internal resistance identification method further includes: The internal resistance identification equivalent circuit model is created by using a first-order RC equivalent circuit, and the internal resistance identification equivalent circuit model includes a mathematical model of the open circuit voltage and the battery internal resistance.

8. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the method for identifying the internal resistance of a lithium battery according to any one of claims 1 to 7 is implemented.

9. An electronic device, It is characterized in that include: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the lithium battery internal resistance identification method as described in any one of claims 1 to 7.

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