Battery model parameter identification method and device, electronic equipment and storage medium

Abstract

The invention belongs to the technical field of battery parameter identification, and particularly relates to a battery model parameter identification method and device, electronic equipment and a storage medium. A multi-information theory is introduced into the memory-limited recursive least square algorithm, an identification model is determined, the multi-information theory is used for obtaining information length data to serve as input of the identification model, and the memory-limited recursive least square algorithm is used for limiting the memory length of the identification model; based on the identification model, the real-time voltage and the real-time current, parameter identification is carried out on the second-order RC battery model, the identification model has better tracking performance and stability, the identification result can quickly approach and stably converge near a reference value, and a parameter identification result with a better effect can be obtained.

Description

Technical Field

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

[0002] At present, the parameter identification methods of lithium-ion battery models based on equivalent circuit models are mainly divided into two research directions: offline parameter identification and online parameter identification.

[0003] Online parameter identification can process data while the battery is running, thereby identifying the parameters of the model in real time. Compared with offline parameter identification, online parameter identification can dynamically adapt to the working state and working conditions of the battery, which is one of the reasons why many scholars at home and abroad are committed to exploring online parameter identification.

[0004] Among the online parameter identification methods of equivalent circuit models, the recursive least squares (RLS) algorithm is the most common one. This method has small computational complexity and is simple and easy to implement. However, the data memory length of this method is infinite and the data weights are equal. As the data length increases, the accumulation of old data causes the algorithm to have poor tracking performance. At the same time, the covariance matrix gradually tends to zero, which makes the correction coefficient gradually tend to zero, so that the algorithm loses its correction ability. This phenomenon is called "data saturation", which also limits the application of the RLS algorithm in practice. Therefore, some scholars have proposed the Forgetting Factor Recursive Least Squares (FFRLS) algorithm, which effectively improves and solves the poor tracking and "data saturation" problems of the RLS algorithm. However, it is difficult to select a suitable forgetting factor value, which causes the problem of poor stability of the FFRLS algorithm.

[0005] Therefore, how to solve the defects of poor tracking and poor stability in the online parameter identification process of the equivalent circuit model has become an urgent problem to be solved. Summary of the invention

[0006] The present application provides a battery model parameter identification method, device, equipment and medium to solve the defects of poor tracking and poor stability in the online parameter identification process of the equivalent circuit model.

[0007] In a first aspect, an embodiment of the present application provides a battery model parameter identification method, the method comprising: introducing multiple innovation theory into a recursive least squares algorithm with limited memory to determine an identification model, the multiple innovation theory is used to obtain innovation length data as input of the identification model, the recursive least squares algorithm with limited memory is used to limit the memory length of the identification model; based on the identification model, real-time voltage and real-time current, performing parameter identification on a second-order RC battery model.

[0008] In one possible implementation, the recursive least squares algorithm with limited memory is used to limit the memory length of the identification model, including: obtaining the input observation matrix and the output observation matrix corresponding to the preset memory length L and the time k to be eliminated; determining the covariance matrix corresponding to the input observation matrix and the output observation matrix based on the input observation matrix and the output observation matrix; determining the expansion of the covariance matrix based on the covariance matrix and the matrix inversion formula; determining the recursive least squares algorithm with limited memory based on the expansion of the covariance matrix and the time k to be eliminated.

[0009] In one possible implementation, the multi-innovation theory is introduced into the recursive least squares algorithm with limited memory, and the identification model is determined, including: based on a preset multi-innovation length p, the time k to be eliminated, the multi-innovation theory and parameter estimation values, the multi-innovation input vector, the multi-innovation output vector and the multi-innovation error vector are determined; based on the multi-innovation output vector and the recursive least squares update formula, the initial identification model is determined; based on the initial identification model, the time k to be eliminated and the matrix inversion operation formula, the identification model is determined.

[0010] In one possible implementation, before obtaining the new information length data, it is necessary to determine a preset multi-new information length p, and the method for determining the preset multi-new information length p includes: based on a preset simulation software, simulating a first preset operating condition and a second preset operating condition, the first preset operating condition and the second preset operating condition are used to determine the second-order RC battery model line parameter identification data; by a preset step length, determining the calculation result of the second-order RC battery model line parameter identification data; based on the calculation result, determining the preset multi-new information length p.

[0011] In a possible implementation, before performing parameter identification on the second-order RC battery model based on the identification model, a battery experiment needs to be performed, and the battery experiment includes testing the available capacity of the battery, HPPC cycle condition test, FUDS cycle condition test and DST cycle condition test.

[0012] In a possible implementation, the preset simulation software is Matlab simulation software, the first preset operating condition is a full-city driving condition, and the second preset operating condition is a dynamic stress test condition.

[0013] In a possible implementation, before conducting a battery experiment, a battery test experimental platform needs to be built, and the battery test experimental platform includes a host computer, a battery detection system and a constant temperature box, and the temperature of the constant temperature box is 25°C.

[0014] In the second aspect, an embodiment of the present application provides a battery model parameter identification method device, which includes: a construction module, used to introduce multiple innovation theory into a recursive least squares algorithm with limited memory to determine an identification model, the multiple innovation theory is used to obtain innovation length data as input of the identification model, and the recursive least squares algorithm with limited memory is used to limit the memory length of the identification model; an identification module, used to perform parameter identification on a second-order RC battery model based on the identification model, real-time voltage and real-time current.

[0015] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in the first aspect or any one of the implementation methods thereof is implemented.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect or any one of the implementation methods thereof is implemented.

[0017] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on an electronic device, the electronic device executes the method described in the first aspect or any one of the implementation methods thereof.

[0018] The solution provided in this application introduces the multi-new information theory into the recursive least squares algorithm with limited memory to determine the identification model. The multi-new information theory is used to obtain the new information length data as the input of the identification model, and the recursive least squares algorithm with limited memory is used to limit the memory length of the identification model; based on the identification model, real-time voltage and real-time current, the second-order RC battery model is parameter identified, which has better tracking than the RLS algorithm and better stability than the FFRLS algorithm. The identification result can quickly approach and stably converge to the reference value. The use of this algorithm can produce better parameter identification results; because the SOC of the battery cannot be obtained directly through instrument measurement, it is usually necessary to establish a battery model and rely on the battery model to estimate it. Therefore, a good parameter identification result can more accurately estimate the SOC value of the battery. Accurate SOC estimation can correctly display the current remaining power and estimated range of the electric vehicle, helping the driver to make relevant preparations in advance and avoid vehicle failure due to insufficient power; accurate SOC estimation can also prevent the battery from overcharging or over-discharging, which not only avoids potential system interruption risks and internal battery damage, but also reduces the risks of shortened life or explosion; in addition, accurate SOC estimation can ensure the maximum utility of the battery, reduce costs and prevent waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A flowchart of a battery model parameter identification method provided in one embodiment of the present application;

[0021] Figure 2 is the available capacity of the battery at different current rates provided by an embodiment of the present application;

[0022] Figure 3 A graph showing the HPPC cycle operating condition test current and battery terminal voltage response provided in one embodiment of the present application;

[0023] Figure 4 A fitting curve diagram of the OCV-SO relationship point provided in an embodiment of the present application;

[0024] Figure 5 This is a diagram of the identification result of the ohmic internal resistance R0 under the FUDS cycle condition provided by an embodiment of the present application;

[0025] Figure 6is a comparison diagram between the simulated terminal voltage and the actual terminal voltage under the FUDS cycle condition provided by an embodiment of the present application;

[0026] Figure 7 is a terminal voltage error diagram under FUDS cycle conditions provided by an embodiment of the present application;

[0027] Figure 8 This is a diagram of the identification result of the ohmic internal resistance R0 under the DST cycle condition provided by an embodiment of the present application;

[0028] Fig. 9 It is a comparison diagram of the simulated terminal voltage and the actual terminal voltage under the DST cycle condition provided by an embodiment of the present application;

[0029] Fig.10 is a terminal voltage error diagram under a DST cycle condition provided by an embodiment of the present application;

[0030] Fig.11 A structural block diagram of a battery model parameter identification device provided in one embodiment of the present application;

[0031] Fig.12 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0032] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0033] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0034] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0035] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0036] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0037] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0038] At present, the parameter identification methods of lithium-ion battery models based on equivalent circuit models are mainly divided into two research directions: offline parameter identification and online parameter identification.

[0039] Offline parameter identification usually relies on pulse current testing. During the conversion of the battery from the charge and discharge state to the zero current state, the battery voltage has fast and slow change stages. The fast voltage change is mainly caused by the internal resistance of the battery, while the slow change is related to the polarization effect of the battery. By analyzing these changes, the test data can be calculated and curve fitted to determine the relevant parameters of the equivalent circuit model.

[0040] Online parameter identification can process data while the battery is running, thereby identifying the parameters of the model in real time. Compared with offline parameter identification, online parameter identification can dynamically adapt to the working state and working conditions of the battery, which is one of the reasons why many scholars at home and abroad are committed to exploring online parameter identification.

[0041] Offline parameter identification usually relies on pulse current testing. During the conversion of the battery from the charge and discharge state to the zero current state, the battery voltage has fast and slow change stages. The fast voltage change is mainly caused by the internal resistance of the battery, while the slow change is related to the polarization effect of the battery. By analyzing these changes, the test data can be calculated and curve fitted to determine the relevant parameters of the equivalent circuit model.

[0042] Among the online parameter identification methods based on equivalent circuit models, the RLS algorithm is the most common one. This method has a small amount of calculation and is simple and easy to implement. However, the data memory length of this method is infinite and the data weights are equal. As the data length increases, the accumulation of old data causes the algorithm to have poor tracking performance. At the same time, the covariance matrix gradually tends to zero, which makes the correction coefficient gradually tend to zero, so that the algorithm loses its correction ability. This phenomenon is called "data saturation", which also limits the application of the RLS algorithm in practice. Therefore, some scholars proposed the FFRLS algorithm, which effectively improves and solves the poor tracking and "data saturation" problems of the RLS algorithm. However, it is difficult to select a suitable forgetting factor value because different values ​​will directly affect the performance of the identification algorithm.

[0043] Although the RLS algorithm is simple, efficient, and computationally small, and is very suitable for online parameter identification, it also has certain limitations. The limitations are that the data memory length of the RLS algorithm is infinite and equal weights are given to new and old data. As time goes on, the accumulation of old data causes the algorithm to have poor tracking performance. At the same time, the positive definite covariance matrix in the algorithm will continue to approach zero as the number of recursions increases, causing the correction factor to also approach zero, resulting in the algorithm losing its correction ability, which is called the "data saturation" phenomenon.

[0044] The limited memory recursive least squares algorithm is based on the traditional RLS algorithm and limits the memory length of the data, assuming that L is the data memory length. The principle is that when the data memory length is less than or equal to L, the recursive process is the same as the traditional RLS algorithm; when the data memory length is greater than L (assuming that there are L+1 groups of data at this time, generated by the Kth to K+Lth moments respectively), it is necessary to eliminate the influence of the data at the Kth moment on the result, and only retain the effect of the L groups of data at the K+1st to K+Lth moments.

[0045] Although FFRL effectively improves and solves the poor tracking and "data saturation" problems of RLS, the value of the forgetting factor has a great impact on the algorithm. Studies have shown that when the forgetting factor is larger, the "forgetting ability" of old data is smaller, the stability of the algorithm is better, and the volatility of the parameter identification results is smaller; however, the worse the tracking, the greater the fitting error of the terminal voltage. On the contrary, when the forgetting factor is smaller, the "forgetting ability" of old data is stronger, the algorithm tracking is better, and the fitting error of the terminal voltage is smaller; however, the worse the stability, the more volatile the parameter identification results. Therefore, FFRLS cannot have both good tracking and stability at the same time.

[0046] In order to solve the problems of poor tracking performance of the RLS algorithm and poor stability of the FFRLS algorithm, this application firstly uses the multi-innovation recursive least squares algorithm with limited memory for battery model parameter identification. The memory length of limited data is intended to overcome the "data saturation" problem of the algorithm and improve the tracking performance of the algorithm; multiple innovations are used as inputs of the algorithm to improve the stability of the algorithm.

[0047] Figure 1 A flow chart of a battery model parameter identification method provided in an embodiment of the present application, the method comprising:

[0048] S110, introducing the multi-innovation theory into the recursive least squares algorithm with limited memory to determine the identification model. The multi-innovation theory is used to obtain the innovation length data as the input of the identification model. The recursive least squares algorithm with limited memory is used to limit the memory length of the identification model.

[0049] In one possible implementation, a recursive least squares algorithm with limited memory is used to limit the memory length of the identification model, including: obtaining an input observation matrix and an output observation matrix corresponding to a preset memory length L and a time k to be eliminated; determining a covariance matrix corresponding to the input observation matrix and the output observation matrix based on the input observation matrix and the output observation matrix; determining an expansion of the covariance matrix based on the covariance matrix and a matrix inversion formula; determining a recursive least squares algorithm with limited memory based on the expansion of the covariance matrix and the time k to be eliminated.

[0050] For example, assume that L is the number of preset memory length groups. When the memory length is less than or equal to L, the recursive process is the same as the traditional RLS algorithm; when the data memory length is greater than L (assuming that there are L+1 groups of data, generated by the Kth to K+Lth moments respectively), it is necessary to eliminate the influence of the Kth moment data on the result, and only retain the effect of the L groups of data from the K+1th to K+Lth moments.

[0051] When the memory length is less than or equal to L, the recursive formula is the same as RLS.

[0052] Assume that the input-output relationship of the system model can be described in the form of formula (1):

[0053] y=θ 1 x 1 +θ 2 x 2 +…+θ n x n +e (1)

[0054] In the formula, x 1 ,x 2 ,......,x n θ represents the input of the system, and y represents the output of the system. 1 ,θ 2 , ..., θ n is the parameter to be estimated, e represents the noise, let θ T =[θ 1 ,θ 2 ,.....,θ n ].

[0055] Formula (1) is expressed in matrix form as shown in formula (2).

[0056] Y k =H k θ+V k (2)

[0057] In the formula, k represents the kth group of observed data.

[0058] The error function is defined as shown in formula (3):

[0059]

[0060] The error function J(θ) is minimized as shown in formula (4):

[0061]

[0062] In the formula, When , the error function J(θ) reaches its minimum value.

[0063] Right now The formula (5) must be satisfied:

[0064]

[0065] According to the above formula, the estimated parameter value of the system at time k can be obtained As shown in formula (6):

[0066]

[0067] but is the LS solution of the system at time k.

[0068] make The recursive calculation formula of RLS can be further obtained, as shown in formula (7):

[0069]

[0070] In the formula, is the estimated value at time k, K(k) is the correction factor at time k, P(k) is the covariance matrix at time k, is the system input vector at time k, y(k) is the system observation value at time k, and I is the unit matrix.

[0071] When the memory length is greater than L, the RLS recursive rule is first used to update the estimation result, as shown in formula (8).

[0072]

[0073] In the formula, represents the estimated value of L+1 groups at time k~k+L, K(k,k+L) represents the correction factor of L+1 groups at time k~k+L, P(k,k+L) represents the covariance matrix of L+1 groups at time k~k+L, represents the system input vector of L+1 groups from k to k+L time points, y(k+L) represents the system observation values ​​of L+1 groups from k to k+L time points, and I is the unit matrix.

[0074] According to the principle of limiting memory to L, the influence of the data at the kth moment on the result needs to be eliminated. k and X k+1 They represent the input observation matrices at time k~k+L and time k+1~k+L respectively, Representing the output observation matrix at time k~k+L and time k+1~k+L, we have:

[0075]

[0076] According to the matrix inversion operation formula, we have:

[0077] (A+BCD) -1 =A -1 -A -1 B(C -1 +DA -1 B) -1 DA -1 (10)

[0078] According to equations (9) and (10), P(k+1,k+L) can be expanded as:

[0079]

[0080] After further arrangement, the recursive expression that eliminates the effect of the k-th moment data is obtained as follows:

[0081]

[0082] The limited memory recursive least squares algorithm can theoretically effectively solve the "data saturation" problem of the RLS algorithm and improve the tracking ability of the algorithm, but the limited memory rule will make the stability of the algorithm worse. Multi-information input uses the data of the most recent multiple sampling moments as the input of the model, and more information data as the basis for reasoning, which can theoretically effectively improve the stability of the algorithm.

[0083] In one possible implementation, the multi-innovation theory is introduced into the recursive least squares algorithm with limited memory, and the identification model is determined, including: based on a preset multi-innovation length p, the time k to be eliminated, the multi-innovation theory and parameter estimation values, the multi-innovation input vector, the multi-innovation output vector and the multi-innovation error vector are determined; based on the multi-innovation output vector and the recursive least squares update formula, the initial identification model is determined; based on the initial identification model, the time k to be eliminated and the matrix inversion operation formula, the identification model is determined.

[0084] This application defines the multi-innovation length as p, the input vector φ(p, k), the output vector Y(p, k) and the error vector V(p, k), as follows:

[0085]

[0086] Where: y(ki) represents the input and output observation data at time ki, where (i=1, 2, ..., p-1).

[0087] Assume that the parameter estimation result at the kth moment is Then formula (13) satisfies:

[0088]

[0089] According to equations (8) and (14), the recursive calculation of updating the estimated parameters using multiple innovations in the multi-innovation recursive least squares algorithm with limited memory is:

[0090]

[0091] In the formula, is the parameter estimation matrix obtained by applying L+1 sets of data at time k~k+L under the multi-innovation rule. The corresponding covariance matrix and gain matrix are K(k,k+L)p and P(k,k+L) p .

[0092] Similarly, it is necessary to eliminate the influence of the k-th moment data on the result. The principle is the same as formula (11). Therefore, the recursive expression for eliminating the effect of the k-th moment data under the multi-innovation rule is:

[0093]

[0094] When the data memory length is less than or equal to L, it is only necessary to use the recursive form of formula (15) to update the parameter estimation. When the data memory length is greater than L, the parameter estimation result is first updated according to formula (15), and then the influence of the k-th moment data is eliminated according to formula (16), retaining the effect of the latest L groups of observation data on the parameter estimation result.

[0095] In one possible implementation, before obtaining the new information length data, it is necessary to determine the preset multi-new information length p, and the method for determining the preset multi-new information length p includes: based on a preset simulation software, simulating a first preset operating condition and a second preset operating condition, the first preset operating condition and the second preset operating condition are used to determine the second-order RC battery model line parameter identification data; by a preset step length, determining the calculation result of the second-order RC battery model line parameter identification data; based on the calculation result, determining the preset multi-new information length p.

[0096] Specifically, the preset simulation software is Matlab simulation software, the first preset working condition is a full urban driving schedule (FUDS) cycle working condition, and the second preset working condition is a dynamic stress test (DST) cycle working condition.

[0097] S120, performing parameter identification on the second-order RC battery model based on the identification model, the real-time voltage, and the real-time current.

[0098] In actual application, this algorithm can be used to estimate the parameter values ​​in the second-order battery RC model in real time, supporting the subsequent real-time estimation of battery SOC, SOH, etc. It can be used in battery SOC, SOH estimation, predicting battery failures by using estimated parameter data, analyzing the impact of different design schemes on battery performance, etc.

[0099] In a possible implementation, the present application requires the construction of a battery testing experimental platform, which includes a host computer, a battery detection system and a constant temperature box, and the temperature of the constant temperature box is 25°C.

[0100] Specifically, a battery testing experimental platform was built based on a host computer, a high-performance battery detection system and a constant temperature box, and lithium iron phosphate single cells were selected as the research object. Under 25°C temperature control, the battery was charged and discharged, including battery available capacity test, HPPC cycle condition test, FUDS cycle condition test and DST cycle condition test, and OCV was obtained based on the HPPC cycle condition test experimental data: thus obtaining the open circuit voltage-state of charge relationship curve, that is, the OCV-SOC relationship curve.

[0101] Battery available capacity test

[0102] The nominal capacity of a lithium-ion battery refers to the maximum amount of electricity that the battery can release under specific conditions (usually the standard charging and discharging method specified by the battery manufacturer). However, in actual battery operation, the charging and discharging methods do not always follow the standard conditions specified by the battery manufacturer; at the same time, due to factors such as battery materials and production processes, there may be inconsistencies in the available capacity even between single cells of the same type and batch; in addition, the setting of capacity parameters has a great impact on the subsequent HPPC cycle condition test, parameter identification research, and charge state estimation research of this application. Therefore, it is necessary to test the available capacity of the battery. Figure 2 It is the available capacity of the battery under different current rates provided in an embodiment of the present application.

[0103] HPPC cycle test

[0104] The HPPC test applies a specific pulse current to the battery for charging and discharging to obtain the terminal voltage response of the battery. The original intention was to evaluate and test the dynamic power performance of the battery. Since this method can obtain the parameters of the lithium-ion battery equivalent circuit model by analyzing and calculating the battery current and terminal voltage response, this method was later widely used for offline parameter identification of battery equivalent models. Figure 3 This is the HPPC cycle condition test current and battery terminal voltage response provided in one embodiment of the present application.

[0105] OCV-SOC curve acquisition

[0106] From the HPPC cycle test experimental steps and processes, we can know that before each HPPC test, the battery has been standing for a long enough time, and it can be considered that the battery polarization effect has disappeared and the battery has reached a fully balanced state. At this time, the battery terminal voltage can be considered to be the battery open circuit voltage OCV. Figure 4 It is an OCV-SOC fitting curve provided in an embodiment of the present application.

[0107] Furthermore, several common lithium-ion equivalent circuit models are analyzed, and the second-order RC model is selected as the research basis of this application.

[0108] Specifically, the equivalent circuit model simulates the performance and behavior of the battery under actual working conditions by using a combination of basic circuit elements (such as resistors and capacitors). This model can intuitively represent the various characteristics of the battery's working state. Each parameter of the model can be obtained through identification, and then the battery SOC can be estimated through the battery model. The second-order RC model not only simulates the resistance effect of the battery, but also distinguishes between the electrochemical polarization and concentration polarization phenomena of the battery. The model has a relatively high accuracy and parameter identification is relatively easy, taking into account issues such as the accuracy of the model and the difficulty of parameter identification.

[0109] Furthermore, in order to verify the good stability and traceability of the online parameter identification of the multi-information recursive least squares method with limited memory, this application uses comparative tests to prove, through real-time voltage and real-time current, that the multi-information recursive least squares method with limited memory has better stability and traceability than the RLS algorithm and the FFRLS algorithm.

[0110] The memory length and innovation length of the data directly affect the performance of the algorithm. Based on experience, this application sets the memory length to L = 500. Then, based on Matlab simulation software, the FUDS cycle condition and the DST cycle condition are used as simulation conditions, and the multi-innovation recursive least squares algorithm with limited memory and different innovation lengths is used to perform online parameter identification simulation on the second-order RC model. The data length value P is determined by the simulation results.

[0111] Since the dimension of the innovation matrix increases with the increase of the innovation length, the computational complexity of parameter identification increases sharply. Therefore, this paper limits the optimization range of the innovation length p value to {p∈Z|1≤p≤10}. The error index of the simulated terminal voltage and the actual terminal voltage is used to evaluate the quality of the innovation length. Two statistical indicators are introduced: Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The calculation methods are shown in formula (17):

[0112]

[0113] Where n is the number of samples, y i is the actual value, is an estimate. MAE will not have the situation where positive and negative error values ​​offset each other, and can better reflect the overall error between the estimate and the actual value. The smaller the MAE value, the more accurate the estimate is overall. RMSE is the square root of the average square of the error, and gives a higher weight to large errors. Therefore, RMSE is more sensitive to larger errors. If there are large values ​​in the prediction error, the RMSE will be correspondingly larger. Similarly, the smaller the RMSE, the more accurate the overall estimate is, and the more concentrated the error is.

[0114] The optimization results for the value of innovation length p are shown in Table 1 and Table 2:

[0115] Table 1 Optimization results of the innovation length p value under FUDS cycle conditions

[0116]

[0117]

[0118] Table 2 Optimization results of the innovation length p value under DST cycle conditions

[0119] p-value 1 2 3 4 5 MAE value(mV) 11.2 4.8 3.6 3.0 2.7 RMSE value (mV) 14.4 7.0 5.8 5.2 4.9 p-value 6 7 8 9 10 MAE value(mV) 2.5 2.3 2.2 2.1 2.0 RMSE value (mV) 4.7 4.5 4.4 4.3 4.2

[0120] According to the optimization results under the two working conditions in Table 1 and Table 2, it can be seen that as the innovation length p increases, the MAE value and RMSE value of the terminal voltage gradually decrease, which means that as the innovation length p increases, the algorithm estimates the terminal voltage more and more accurately. However, before p is less than , the MA value and RMSE value of the terminal voltage decrease much more with the increase of p than after p is greater than 5; at the same time, as the innovation length p increases, the dimension of the state matrix increases, and the amount of calculation and the complexity of calculation increase sharply.

[0121] Therefore, considering the accuracy of the algorithm in estimating the battery terminal voltage and the complexity of the algorithm calculation, this patent selects the new information length p=5.

[0122] Based on Matlab simulation software, the FUDS cycle condition and DST cycle condition were used as simulation conditions respectively, and the RLS, FFRLS and multi-innovation recursive least squares algorithm with limited memory were used to carry out online parameter identification simulation and comparative analysis of the second-order RC model.

[0123] FUDS cycle condition simulation and comparative analysis:

[0124] The identification result of the FFRLS algorithm first approaches the reference value (about 25mΩ), followed by the multi-innovation recursive least squares algorithm with limited memory, and the RLS algorithm is the last, indicating that the FFRLS algorithm has the best tracking performance, followed by the multi-innovation recursive least squares algorithm with limited memory, and the RLS algorithm is the worst. However, the identification result of the FFRLS algorithm fluctuates throughout the process and cannot stably converge to the reference value. Even when the SOC is less than 50%, the ohmic internal resistance identification result deviates more and more from the reference value, indicating that the stability of the FFRLS algorithm is poor; while the identification results of the multi-innovation recursive least squares algorithm with limited memory and the RLS algorithm can gradually converge and stabilize near the reference value, indicating that the two algorithms have good stability.

[0125] The FFRLS algorithm has the best fitting effect on the actual terminal voltage, and the overall error of the terminal voltage is the smallest; the multi-innovation recursive least squares algorithm with limited memory is second, and the overall error of the terminal voltage of the RLS algorithm is the largest. Before about 300s, the errors of the three algorithms are relatively large because the identification results have not approached the actual value; and after about 7000s, the errors of the three algorithms are relatively large because the battery model cannot accurately describe the internal characteristics of the battery at low SOC.

[0126] The terminal voltage MAE and RMSE values ​​corresponding to the FFRLS algorithm are the smallest, at 1.2mV and 2.6mV respectively. The second is the multi-innovation recursive least squares algorithm with limited memory, at 3.4mV and 6.0mV respectively. The last is the RLS algorithm, at 6.5mV and 9.1mV respectively.

[0127] According to the above analysis, under the FUDS cycle condition, the multi-innovation recursive least squares algorithm with limited memory introduced in this application has better tracking performance than the RLS algorithm, and can better track the battery voltage. The MAE value and RMSE value of the terminal voltage are reduced by about 47.7% and 34.1% respectively; although the tracking performance is not as good as the FFRLS algorithm, it has better stability than the FFRLS algorithm, and its identification result can gradually and stably converge to the reference value. Figure 5 is the ohmic internal resistance R under the FUDS cycle condition provided in one embodiment of the present application 0 The identification result diagram of Figure 6 is a comparison diagram between the simulated terminal voltage and the actual terminal voltage under the FUDS cycle condition provided by an embodiment of the present application, Figure 7 This is a terminal voltage error diagram under FUDS cycle conditions provided by an embodiment of the present application.

[0128] DST cycle condition simulation and comparative analysis:

[0129] Similar to the FUDS cycle condition, the FFRLS algorithm identification result is the first to approach the reference value (about 25mΩ), followed by the limited memory multi-innovation recursive least squares algorithm, and the RLS algorithm is the last, indicating that the FFRLS algorithm has the best tracking performance, followed by the limited memory multi-innovation recursive least squares algorithm, and the RLS algorithm is the worst. However, the FFRLS algorithm identification result fluctuates throughout the process and cannot stably converge to the reference value. Even when the SOC is less than 50%, the identification result deviates more and more from the reference value, indicating that the FFRLS algorithm has poor stability; while the limited memory multi-innovation recursive least squares algorithm and the RLS algorithm identification results can gradually converge and stabilize near the reference value, indicating that the two algorithms have good stability.

[0130] The FFRLS algorithm has the strongest ability to track the actual terminal voltage, followed by the multi-innovation recursive least squares algorithm with limited memory, and the RLS algorithm is the worst. In other words, the FFRLS algorithm has the best tracking performance among the three, followed by the multi-innovation recursive least squares algorithm with limited memory, and the RLS algorithm is the worst.

[0131] The terminal voltage MAE and RMSE values ​​corresponding to the FFRLS algorithm are the smallest, at 0.6mV and 2.4mV respectively. The second is the multi-innovation recursive least squares algorithm with limited memory, at 2.7mV and 4.9mV respectively. The last is the RLS algorithm, at 5.6mV and 8.1mV respectively.

[0132] Under the DST cycle condition, the multi-innovation recursive least squares algorithm with limited memory introduced in this application has better tracking performance than the RLS algorithm, and can better track the battery voltage. The MAE value and RMSE value of the terminal voltage are reduced by about 51.8% and 39.5%, respectively; although the tracking performance is not as good as the FFRLS algorithm, it has better stability than the FFRLS algorithm, and its identification results can gradually and stably converge to near the reference value. Figure 8 is an identification result diagram of the ohmic internal resistance R0 under the DST cycle condition provided by an embodiment of the present application, Fig. 9 is a comparison diagram of the simulated terminal voltage and the actual terminal voltage under the DST cycle condition provided by an embodiment of the present application, Fig.10 This is a terminal voltage error diagram under a DST cycle condition provided in an embodiment of the present application.

[0133] The solution provided in the present application introduces the multi-new information theory into the recursive least squares algorithm based on limited memory to determine the identification model. The multi-new information theory is used to obtain the new information length data as the input of the identification model, and the recursive least squares algorithm with limited memory is used to limit the memory length of the identification model; based on the identification model, the second-order RC battery model is subjected to parameter identification, which has better tracking than the RLS algorithm and better stability than the FFRLS algorithm, and the identification result can quickly approach and stably converge to the vicinity of the reference value. The use of this algorithm can produce better parameter identification results; because the SOC of the battery cannot be directly obtained through instrument measurement, it is usually necessary to estimate it by establishing a battery model and relying on the battery model. Therefore, a good parameter identification result can more accurately estimate the SOC value of the battery. Accurate SOC estimation can correctly display the current remaining power and estimated range of the electric vehicle, helping the driver to make relevant preparations in advance and avoid vehicle failure due to insufficient power; accurate SOC estimation can also prevent the battery from overcharging or over-discharging, which not only avoids potential system interruption risks and internal battery damage, but also reduces the risks of shortened life or explosion; in addition, accurate SOC estimation can ensure the maximum utility of the battery, reduce costs and prevent waste of resources.

[0134] Fig.11 This is a structural block diagram of a battery model parameter identification device provided by an embodiment of the present application. For the sake of convenience, only the part related to the embodiment of the present application is shown. Fig.11 The battery model parameter identification device 1100 includes a construction module 1101 and an identification module 1102 .

[0135] In one implementation, the apparatus 1100 may be used to implement the above Figure 1 For example, the construction module 1101 is used to implement S110, and the identification module 1102 is used to implement S120.

[0136] The solution provided in the present application introduces the multi-new information theory into the recursive least squares algorithm based on limited memory to determine the identification model. The multi-new information theory is used to obtain the new information length data as the input of the identification model, and the recursive least squares algorithm with limited memory is used to limit the memory length of the identification model; based on the identification model, real-time voltage and real-time current, the second-order RC battery model is parameter identified, which has better tracking than the RLS algorithm and better stability than the FFRLS algorithm. The identification result can quickly approach and stably converge to the reference value. The use of this algorithm can produce better parameter identification results; because the SOC of the battery cannot be directly obtained through instrument measurement, it is usually necessary to establish a battery model and rely on the battery model to estimate it. Therefore, a good parameter identification result can more accurately estimate the SOC value of the battery. Accurate SOC estimation can correctly display the current remaining power and estimated range of the electric vehicle, helping the driver to make relevant preparations in advance and avoid vehicle failure due to insufficient power; accurate SOC estimation can also prevent the battery from overcharging or over-discharging, which not only avoids potential system interruption risks and internal battery damage, but also reduces the risks of shortened life or explosion; in addition, accurate SOC estimation can ensure the maximum utility of the battery, reduce costs and prevent waste of resources.

[0137] Fig.12 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Fig.12 As shown, the electronic device 12 of this embodiment includes: at least one processor 120 ( Fig.12 Only one is shown in the figure) a processor, a memory 121, and a computer program 122 stored in the memory 121 and executable on the at least one processor 120, and when the processor 120 executes the computer program 122, the steps in any of the above method embodiments are implemented.

[0138] The electronic device 12 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device may include, but is not limited to, a processor 120 and a memory 121. Those skilled in the art will appreciate that Fig.12 It is only an example of the electronic device 12 and does not constitute a limitation on the electronic device 12. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0139] The processor 120 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0140] In some embodiments, the memory 121 may be an internal storage unit of the electronic device 12, such as a hard disk or memory of the electronic device 12. In other embodiments, the memory 121 may also be an external storage device of the electronic device 12, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 12. Further, the memory 121 may also include both an internal storage unit of the electronic device 12 and an external storage device. The memory 121 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 121 may also be used to temporarily store data that has been output or is to be output.

[0141] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0142] An embodiment of the present application also provides an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps of any of the above-mentioned method embodiments when executing the computer program.

[0143] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0144] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0145] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0146] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0147] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0148] In the embodiments provided in the present application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0149] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0150] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A battery model parameter identification method, characterized in that: The method comprises: Introducing multiple innovation theory into the limited memory recursive least squares algorithm to determine the identification model, the multiple innovation theory is used to obtain innovation length data as input of the identification model, and the limited memory recursive least squares algorithm is used to limit the memory length of the identification model; Based on the identification model, the real-time voltage and the real-time current, parameter identification is performed on the second-order RC battery model.

2. The method according to claim 1, characterized in that The memory-limited recursive least squares algorithm is used to limit the memory length of the identification model, including: Obtain the input observation matrix and output observation matrix corresponding to the preset memory length L and the time k to be eliminated; Based on the input measurement matrix and the output measurement matrix, determining the covariance matrix corresponding to the input measurement matrix and the output measurement matrix; Determine an expansion of the covariance matrix based on the covariance matrix and a matrix inversion formula; Based on the expansion of the covariance matrix and the time k to be eliminated, a recursive least squares algorithm with limited memory is determined.

3. The method according to claim 2, characterized in that The multi-innovation theory is introduced into the recursive least squares algorithm with limited memory, and the identification model is determined to include: Determine a multi-innovation input vector, a multi-innovation output vector and a multi-innovation error vector based on a preset multi-innovation length p, the time k to be eliminated, the multi-innovation theory and parameter estimation values; Determining a primary identification model based on the multiple innovation output vectors and a recursive least squares update formula; An identification model is determined based on the primary identification model, the time k to be eliminated, and the matrix inversion operation formula.

4. The method according to claim 1, characterized in that Before acquiring the innovation length data, it is necessary to determine the preset multi-innovation length p. The method for determining the preset multi-innovation length p includes: Based on the preset simulation software, simulating the first preset operating condition and the second preset operating condition, wherein the first preset operating condition and the second preset operating condition are used to determine the second-order RC battery model line parameter identification data; Determining the calculation result of the second-order RC battery model line parameter identification data by presetting the step length; Based on the calculation result, a preset multi-innovation length p is determined.

5. The method according to claim 1, characterized in that Before performing parameter identification on the second-order RC battery model based on the identification model, a battery experiment needs to be performed, and the battery experiment includes testing the available capacity of the battery, HPPC cycle condition test, FUDS cycle condition test and DST cycle condition test.

6. The method according to claim 4, characterized in that The preset simulation software is Matlab simulation software, the first preset working condition is a full-city driving working condition, and the second preset working condition is a dynamic stress test working condition.

7. The method according to claim 5, characterized in that Before conducting a battery experiment, a battery test experimental platform needs to be built. The battery test experimental platform includes a host computer, a battery detection system and a constant temperature box. The temperature of the constant temperature box is 25°C.

8. A battery model parameter identification method and device, characterized in that: The device comprises: A construction module is used to introduce a multi-innovation theory into a recursive least squares algorithm with limited memory to determine an identification model, wherein the multi-innovation theory is used to obtain innovation length data as an input of the identification model, and the recursive least squares algorithm with limited memory is used to limit the memory length of the identification model; The identification module is used to perform parameter identification on the second-order RC battery model based on the identification model, the real-time voltage and the real-time current.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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