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

Abstract

The application belongs to the technical field of battery parameter identification, and particularly relates to a battery model parameter identification method and device, an electronic device and a storage medium. A multiple innovation theory is introduced into a recursive least square 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 square 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, parameter identification is performed on a second-order RC battery model. The identification model has better tracking and stability, the identification result can quickly approach and stably converge around a reference value, and a better parameter identification result can be obtained.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of battery parameter identification, and particularly relates to a battery model parameter identification method and device, an electronic device and a storage medium. BACKGROUND

[0002] At present, the lithium ion battery model parameter identification method based on the equivalent circuit model mainly includes offline parameter identification and online parameter identification.

[0003] Online parameter identification can process data while the battery is running, so as to identify 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 condition of the battery, which is one of the reasons why many scholars at home and abroad are committed to exploring online parameter identification.

[0004] In the online parameter identification method of the equivalent circuit model, the Recursive Least Squares (RLS) algorithm is the most common method, which has small calculation amount and is simple and easy to implement. However, the data memory length of this method is infinite and the data weight is equal, and with the increase of data length, the accumulation of old data causes poor tracking performance of the algorithm. At the same time, the covariance matrix gradually tends to zero, so that the correction coefficient also gradually tends to zero, so that the algorithm loses the 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 performance and "data saturation" problem 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 performance and poor stability in the online parameter identification process of the equivalent circuit model has become a problem to be solved. SUMMARY

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

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

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

[0009] In a possible implementation, introducing the multiple innovation theory into the recursive least square algorithm with limited memory to determine the identification model comprises: determining a multiple innovation input vector, a multiple innovation output vector and a multiple innovation error vector based on a preset multiple innovation length p, the time k to be removed, the multiple innovation theory and a parameter estimation value; determining a primary identification model based on the multiple innovation output vector and a recursive least square method update formula; and determining the identification model based on the primary identification model, the time k to be removed and the matrix inversion operation formula.

[0010] In a possible implementation, before obtaining the innovation length data, a preset multiple innovation length p needs to be determined, and a method for determining the preset multiple innovation length p comprises: simulating a first preset working condition and a second preset working condition based on a preset simulation software, wherein the first preset working condition and the second preset working condition are used to determine second-order RC battery model linear parameter identification data; determining a calculation result of the second-order RC battery model linear parameter identification data through a preset step length; and determining the preset multiple innovation length p based on the calculation result.

[0011] In a possible implementation, before identifying the parameters of the second-order RC battery model based on the identification model, a battery experiment needs to be performed, and the battery experiment comprises testing a usable capacity of the battery, HPPC cycle working condition testing, FUDS cycle working condition testing and DST cycle working condition testing.

[0012] In a possible implementation, the preset simulation software is a 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.

[0013] In a possible implementation, before the battery experiment is performed, a battery test experiment platform needs to be built, the battery test experiment platform comprising a host computer, a battery detection system and a thermostat, and the temperature of the thermostat being 25 DEG C.

[0014] In a second aspect, the embodiments of the present application provide a battery model parameter identification method and device, the device comprising: a construction module, configured to introduce a multiple innovation theory into a limited memory recursive least square algorithm to determine an identification model, the multiple innovation theory being configured to obtain innovation length data as an input of the identification model, and the limited memory recursive least square algorithm being configured to limit a memory length of the identification model; and an identification module, configured 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, the embodiments of the present application provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements the method according to the first aspect or any of the implementation manners thereof when executing the computer program.

[0016] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method according to the first aspect or any of the implementation manners thereof.

[0017] In a fifth aspect, the embodiments of the present application provide a computer program product, which, when running on an electronic device, causes the electronic device to perform the method according to the first aspect or any of the implementation manners thereof.

[0018] The scheme provided in the application introduces the multiple innovation theory into the recursive least square algorithm with limited memory, determines an identification model, the multiple innovation theory is used to obtain innovation length data as input of the identification model, and the recursive least square 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, parameter identification of a second-order RC battery model is performed, the algorithm has better tracking performance than the RLS algorithm, has better stability than the FFRLS algorithm, and the identification result can quickly approach and stably converge to the vicinity of the reference value. Using the algorithm, a better parameter identification result can be obtained; because the SOC of the battery cannot be directly measured by an instrument, it is usually necessary to estimate the SOC by establishing a 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 cruising range of the electric vehicle, help the driver make relevant preparations in advance, and avoid vehicle failure due to insufficient power; accurate SOC estimation can also prevent overcharging or overdischarging of the battery, which not only avoids potential system interruption risks and internal damage to the battery, but also reduces risks such as shortened life or explosion; in addition, accurate SOC estimation can ensure the maximum utility of the battery, reduce costs and prevent resource waste. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 A flowchart of a battery model parameter identification method provided by an embodiment of the present application is shown in the figure.

[0021] Figure 2 The figure is a graph of the available capacity of the battery under different rate currents provided by an embodiment of the present application.

[0022] Figure 3 The figure is a graph of the HPPC cycle test current and the response of the battery terminal voltage provided by an embodiment of the present application.

[0023] Figure 4 The figure is a graph of the OCV-SO relationship point fitting curve provided by an embodiment of the present application.

[0024] Figure 5 The figure is an identification result graph of the ohmic internal resistance R0 under the FUDS cycle condition provided by an embodiment of the present application.

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

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

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

[0028] Figure 9 is a simulation terminal voltage and an actual terminal voltage comparison chart under a DST cycle working condition provided by an embodiment of the present application;

[0029] Figure 10 is a terminal voltage error chart under a DST cycle working condition provided by an embodiment of the present application;

[0030] Figure 11 is a structural block diagram of a battery model parameter identification device provided by an embodiment of the present application;

[0031] Figure 12 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0032] In the following description, specific details are set forth, such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, persons skilled in the art will understand that the present application can be practiced without these specific details. In other instances, well-known structures, devices, circuits, and methods have not been described in detail in order to avoid obscuring the present application.

[0033] It should be understood that the term "comprising" as used in the specification and in the claims indicates the presence of the recited features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0034] It should also be understood that the term "and / or" as used herein refers to any combination of one or more of the associated listed items, and all possible combinations, and includes these combinations.

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

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

[0037] In the present application, the reference "one embodiment" or "some embodiments" and the like means that the specific features, structures or characteristics described in connection with the embodiment are included in one or more embodiments of the present application. Therefore, the statements "in one embodiment", "in some embodiments", "in other some embodiments", "in further some embodiments" and the like appearing in different places in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "include", "contain", "have" and their variants mean "include but not limited to", unless otherwise specifically emphasized.

[0038] At present, the lithium ion battery model parameter identification method based on equivalent circuit model mainly includes offline parameter identification and online parameter identification.

[0039] Offline parameter identification is usually dependent on pulse current test. During the conversion process of battery from charge-discharge state to zero current state, there are fast and slow change stages of battery voltage. The fast change of voltage is mainly caused by the internal resistance of the battery, and 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, and then the related parameters of the equivalent circuit model can be determined.

[0040] Online parameter identification can process data while the battery is running, so as to identify 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 condition of the battery, which is one of the reasons why many scholars at home and abroad are committed to exploring online parameter identification.

[0041] The off-line parameter identification is usually performed by means of pulse current test. In the process of conversion of the battery from the charge-discharge state to the zero current state, the battery voltage has fast and slow change stages. The fast change of the voltage is mainly caused by the internal resistance of the battery, and 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, and then the related parameters of the equivalent circuit model are determined.

[0042] In the on-line parameter identification method based on the equivalent circuit model, the RLS algorithm is the most common method, which has small calculation amount and is simple and easy to implement. However, the data memory length of this method is infinite and the data weight is equal. With the increase of data length, the accumulation of old data causes poor tracking performance of the algorithm. At the same time, the covariance matrix gradually tends to zero, so that the correction coefficient also gradually tends to zero, so that the algorithm loses the correction ability. This phenomenon is called "data saturation", which also limits the application of RLS algorithm in practice. Therefore, some scholars have proposed the FFRLS algorithm, which effectively improves and solves the poor tracking performance and "data saturation" problem 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 and efficient, has small calculation amount, and is very suitable for on-line parameter identification, it also has certain limitations, which are reflected in the infinite data memory length and equal weight of new and old data. With the passage of time, the accumulation of old data causes poor tracking performance of the algorithm. At the same time, the positive definite covariance matrix in the algorithm will continuously tend to zero with the increase of the recursive times, so that the correction factor also tends to zero, resulting in the loss of correction ability of the algorithm, which is called "data saturation" phenomenon.

[0044] The recursive least square algorithm with limited memory is based on the traditional RLS algorithm, which limits the data memory length. Assume 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, which are generated by the Kth to K+Lth time), the influence of the Kth time data on the result needs to be removed, and only the effect of the K+1th to K+Lth time L groups of data is retained.

[0045] Although the FFRL effectively improves and solves the poor tracking and "data saturation" problems of the RLS, the forgetting factor has a great influence on the algorithm. Research shows that the greater the forgetting factor, the smaller the "forgetting ability" for old data, the better the stability of the algorithm, and the smaller the fluctuation of the parameter identification result; but the tracking is poorer, and the fitting error of the terminal voltage is larger. Conversely, the smaller the forgetting factor, the stronger the "forgetting ability" for old data, the better the tracking of the algorithm, and the smaller the fitting error of the terminal voltage; but the stability is poorer, and the fluctuation of the parameter identification result is more intense. Therefore, the FFRLS cannot simultaneously have good tracking and stability.

[0046] In view of the poor tracking of the RLS algorithm and the poor stability of the FFRLS algorithm, the application first uses a limited memory multiple innovation recursive least square algorithm for battery model parameter identification. The memory length of the limited data is intended to overcome the "data saturation" problem of the algorithm and improve the tracking of the algorithm; multiple innovations are used as inputs of the algorithm, and are intended to improve the stability of the algorithm.

[0047] Figure 1 A flowchart of a battery model parameter identification method provided by an embodiment of the application is shown in the figure. The method comprises:

[0048] S110, introducing a multiple innovation theory into a limited memory recursive least square algorithm to determine an identification model, the multiple innovation theory being used to obtain length data of an innovation as an input of the identification model, and the limited memory recursive least square algorithm being used to limit a memory length of the identification model.

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

[0050] For example, it is assumed that L is a preset memory length group number. When the memory length is less than or equal to L, the recursive process is the same as that of the traditional RLS algorithm; when the data memory length is greater than L (it is assumed that there are L+1 groups of data at this time, which are generated by the times K to K+L), the influence of the data on the result at the time K needs to be removed, and only the effects of the data at the times K+1 to K+L are retained.

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

[0052] Suppose the input-output relationship of a system model can be described in the form of equation (1):

[0053] y = θ1x1+ θ2x2+... + θ n x n + e (1)

[0054] In equation (1), x1, x2,..., x n represent the input of the system, y represents the output of the system. θ1, θ2,..., θ n are parameters to be estimated, e represents noise, and let θ T = [θ1, θ2,..., θ n ].

[0055] Equation (1) can be expressed in matrix form as equation (2).

[0056] Y k = H k θ + V k (2)

[0057] In equation (2), k represents the kth set of observation data.

[0058] Define the error function as equation (3):

[0059]

[0060] Minimize the error function J(θ) as equation (4):

[0061]

[0062] In equation (4), the error function J(θ) reaches a minimum value.

[0063] That is needs to satisfy equation (5):

[0064]

[0065] According to the above equation, the parameter estimation value of the system at time k can be obtained as equation (6):

[0066]

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

[0068] Let the recursive calculation formula of RLS can be further obtained as equation (7): ​

[0069]

[0070] where, is the estimation 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, I is the unit matrix.

[0071] When the memory length is greater than L, the estimation result is updated using the RLS recursive rule, as shown in equation (8).

[0072]

[0073] where, represents the estimation value of the kth~k+Lth time, K(k, k+L) represents the correction factor of the kth~k+Lth time, P(k, k+L) represents the covariance matrix of the kth~k+Lth time, represents the system input vector of the kth~k+Lth time, y(k+L) represents the system observation value of the kth~k+Lth time, I is the unit matrix.

[0074] According to the principle of limiting memory to L, the influence of the kth time data on the result needs to be removed. Let X k and X k+1 represent the input observation matrix of k~k+L and the k+1~k+L time, represent the output observation matrix of k~k+L and the k+1~k+L time, then:

[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 equation (9) and equation (10), P(k+1, k+L) can be expanded as:

[0079]

[0080] Further arrangement can obtain the recursive expression for removing the influence of the kth time data:

[0081]

[0082] The recursive least square algorithm with limited memory 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. The multi-innovation input is to take the data of multiple sampling times as the input of the model, and more information data as the reasoning basis, which can theoretically effectively improve the stability of the algorithm.

[0083] In a possible implementation, the multi-innovation theory is introduced into the recursive least square algorithm with limited memory, and the identification model comprises: determining 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 to be removed k, the multi-innovation theory and the parameter estimation value; determining a primary identification model based on the multi-innovation output vector and a recursive least square algorithm update formula; and determining an identification model based on the primary identification model, the time to be removed k and a matrix inversion operation formula.

[0084] In the application, the multi-innovation length is defined as p, the input vector φ(p, k), the output vector Y(p, k) and the error vector V(p, k) are respectively as follows:

[0085]

[0086] In the formula, y(k-i) respectively represents the input and output observation data at the k-i time, wherein (i = 1, 2,..., p-1). Suppose that the parameter estimation result at the k time is

[0087] Then the formula (13) satisfies:

[0088] According to the formula (8) and the formula (14), the recursive calculation of the multi-innovation recursive least square algorithm with limited memory is that the multi-innovation is used to update the estimated parameters:

[0089]

[0090] In the formula, φ(p, k) = [φ(k-1), φ(k-2),..., φ(k-p+1)]T, Y(p, k) = [y(k-1), y(k-2),..., y(k-p+1)]T and V(p, k) = [v(k-1), v(k-2),..., v(k-p+1)]T.

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

[0092] Similarly, the influence of the data at the k time on the result needs to be removed, and the principle is the same as that of the formula (11), so the recursive expression for removing the action of the data at the k time under the multi-innovation rule is:​

[0093]

[0094] When the data memory length is less than or equal to L, only the recursive form of formula (15) is used to update the parameter estimation; when the data memory length is greater than L, the parameter estimation result is updated according to formula (15) first, and then the influence of the data at the kth moment is eliminated according to formula (16), and the latest L groups of observation data are used to keep the effect on the parameter estimation result.

[0095] In a possible implementation, before the length of the new information is obtained, the preset multiple new information length p is determined, and the method for determining the preset multiple new information length p includes: simulating a first preset working condition and a second preset working condition based on preset simulation software, the first preset working condition and the second preset working condition being used to determine the linear parameter identification data of the second-order RC battery model; determining the calculation result of the linear parameter identification data of the second-order RC battery model through a preset step length; and determining the preset multiple new information length p based on the calculation result.

[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] In S120, the parameters of the second-order RC battery model are identified based on the identification model, the real-time voltage and the real-time current.

[0098] In actual application, the algorithm can be used to estimate the parameter values in the second-order battery RC model in real time, and support the subsequent real-time estimation of the battery SOC and SOH. The algorithm can be used in battery SOC and SOH estimation, prediction of battery failure by using the estimated parameter data, analysis of the influence of different design schemes on the performance of the battery, and the like.

[0099] In a possible implementation, the battery test experiment platform needs to be built, and the battery test experiment platform includes a host computer, a battery detection system and a thermostat, and the temperature of the thermostat is 25℃.

[0100] Specifically, a battery test experimental platform is built based on a host computer, a high-performance battery detection system and a thermostat, and a lithium iron phosphate single battery is selected as a research object. Under 25 DEG C temperature control, the battery is subjected to charge-discharge test, including battery available capacity test, HPPC cycle test, FUDS cycle test and DST cycle test, and OCV is obtained according to HPPC cycle test data, so as to obtain open circuit voltage-charge state relationship curve, i.e. OCV-SOC relationship curve.

[0101] Battery available capacity test

[0102] The nominal capacity of a lithium ion battery refers to the maximum electric quantity that can be released by the battery under specific conditions (usually the standard charge-discharge mode specified by the battery manufacturer). However, in actual work, the charge-discharge mode does not always follow the standard condition specified by the battery manufacturer; at the same time, due to the factors of battery material and production process, there may be inconsistent available capacity between single batteries of the same type and same batch; moreover, the setting of the capacity parameter has a great influence on the subsequent HPPC cycle test, parameter identification research and state of charge estimation research of the application, therefore, it is necessary to test the available capacity of the battery. Figure 2 is the available capacity of the battery under different rate currents provided by an embodiment of the application.

[0103] HPPC cycle test

[0104] HPPC test is a method of charging and discharging the battery by applying a specific pulse current to obtain the terminal voltage response of the battery, and the original intention is to evaluate and test the dynamic power performance of the battery. Since this method can obtain the parameters of the equivalent circuit model of the lithium ion battery by analyzing and calculating the current and terminal voltage response of the battery, it is widely used for offline parameter identification of the equivalent model of the battery. Figure 3 is the HPPC cycle test current and the terminal voltage response of the battery provided by an embodiment of the application.

[0105] OCV-SOC curve acquisition

[0106] As can be seen from the HPPC cycle test experimental steps and processes, the battery has been at rest for a long enough time before each HPPC test, and it can be considered that the polarization effect of the battery has disappeared and the battery has reached a fully balanced state. At this time, it can be considered that the terminal voltage of the battery is the open circuit voltage OCV of the battery. Figure 4 is the OCV-SOC fitting curve provided by an embodiment of the application.

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

[0108] Specifically, the equivalent circuit model is to simulate the performance and behavior of the battery under actual working conditions by using a combination of basic circuit elements (such as resistance, capacitance), which can intuitively represent various characteristics of the battery working state. The parameters of the model can be obtained by identification, and then the SOC of the battery can be estimated by the battery model. The second-order RC model not only simulates the resistance effect of the battery, but also distinguishes the electrochemical polarization and concentration polarization phenomena of the battery, and the model has high accuracy and easy parameter identification. Considering the accuracy of the model and the difficulty of parameter identification, etc.

[0109] Further, in order to verify the good stability and tracking of the limited memory multiple innovation recursive least square method, the present application compares the real-time voltage and real-time current to prove that the limited memory multiple innovation recursive least square method has better stability and tracking than the RLS algorithm and the FFRLS algorithm.

[0110] The selection of the memory length and the innovation length of the data directly affects the performance of the algorithm. According to experience, the memory length is set to L = 500. Then based on the Matlab simulation software, the FUDS cycle condition and the DST cycle condition are used as simulation conditions, and the limited memory multiple innovation recursive least square algorithm with different innovation length 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] Because the dimension of the innovation matrix will increase with the increase of the innovation length, the calculation complexity of the parameter identification will increase sharply, therefore, the optimization range of the innovation length p value is limited to {p∈Z|1≤p≤10}. The error index of the simulation terminal voltage and the actual terminal voltage is used to evaluate the pros and cons of the innovation length, and two statistical indicators are introduced: mean absolute error (Mean Absolute Error, MAE) and root mean square error (Root Mean Square Error, RMSE), the calculation methods are shown in formula (17) respectively:

[0112]

[0113] In the formula, n is the sample number, y i is the actual value, is the estimated value. MAE does not appear positive and negative error values offset, can better reflect the overall error of the estimated value and the actual value. The smaller the MAE value, the more accurate the overall estimate. RMSE is the square of the error average, and gives higher weight to large errors, so RMSE is more sensitive to large errors. If there are large values in the prediction error, the RMSE will be larger accordingly. Similarly, the smaller the RMSE, the more accurate the overall estimate, and the more concentrated the error.

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

[0115] Table 1 FUDS cycle working condition innovation length p value optimization results

[0116]

[0117]

[0118] Table 2 DST cycle working condition innovation length p value optimization results

[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 of Tables 1 and 2 under two working conditions, it can be seen that with the increase of innovation length p, the MAE value and RMSE value of the terminal voltage gradually decrease, which means that with the increase of innovation length p, the algorithm is more and more accurate in estimating the terminal voltage. However, when p is less than 5, the MAE value and RMSE value of the terminal voltage decrease much more than when p is greater than 5; at the same time, with the increase of innovation length p, the dimension of the state matrix increases, and the calculation amount and calculation complexity increase sharply.

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

[0122] Based on Matlab simulation software, under FUDS cycle working condition and DST cycle working condition respectively, RLS, FFRLS and limited memory multi-innovation recursive least square algorithm are used for online parameter identification simulation of second order RC model and comparative analysis.

[0123] FUDS cycle working condition simulation and comparative analysis:

[0124] The identification result of the FFRLS algorithm first approaches the reference value (about 25 mΩ), the multiple innovation recursive least square algorithm with memory limitation is second, and the RLS algorithm is last, which indicates that the tracking performance of the FFRLS algorithm is the best, the multiple innovation recursive least square algorithm with memory limitation is second, 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, and even when the SOC is less than 50%, the ohmic resistance identification result deviates from the reference value more and more, which indicates that the stability of the FFRLS algorithm is poor; the identification results of the multiple innovation recursive least square algorithm with memory limitation and the RLS algorithm can gradually converge and stably converge to the reference value, which indicates that the stability of the two algorithms is good.

[0125] The FFRLS algorithm has the best fitting effect on the actual terminal voltage, and the total error of the terminal voltage is the smallest; the multiple innovation recursive least square algorithm with memory limitation is second, and the total error of the terminal voltage of the RLS algorithm is the largest. Before about 300 s, the errors of the three algorithms are relatively large because the identification result has not approached the actual value; and after about 7000 s, 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 MAE value and the RMSE value of the terminal voltage corresponding to the FFRLS algorithm are the smallest, which are 1.2 mV and 2.6 mV respectively. The multiple innovation recursive least square algorithm with memory limitation is second, which are 3.4 mV and 6.0 mV respectively. The RLS algorithm is last, which are 6.5 mV and 9.1 mV respectively.

[0127] According to the above analysis under the FUDS cycle condition, the multiple innovation recursive least square algorithm with memory limitation introduced in the application has better tracking performance than the RLS algorithm, can better track the battery voltage, and the MAE value and the 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, the multiple innovation recursive least square algorithm with memory limitation has better stability than the FFRLS algorithm, and the identification result can gradually and stably converge to the reference value, Figure 5 is an identification result diagram of the ohmic resistance R0 under the FUDS cycle condition provided by an embodiment of the application, Figure 6 is a comparison diagram of the simulation terminal voltage and the actual terminal voltage under the FUDS cycle condition provided by an embodiment of the application, Figure 7 is a terminal voltage error diagram under the FUDS cycle condition provided by an embodiment of the application.

[0128] DST cycle condition simulation and comparative analysis:

[0129] Similar to the FUDS cycle, the FFRLS algorithm first approaches the reference value (about 25 mΩ), followed by the multiple new recursive least square algorithm with limited memory, and the RLS algorithm last, indicating that the tracking performance of the FFRLS algorithm is the best, followed by the multiple new recursive least square 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 near 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. The identification results of the multiple new recursive least square algorithm with limited memory and the RLS algorithm can gradually converge and stabilize near the reference value, indicating that these two algorithms have good stability.

[0130] The FFRLS algorithm has the strongest tracking ability for the actual terminal voltage, followed by the multiple new recursive least square algorithm with limited memory, and the RLS algorithm is the worst. That is, the tracking performance of the FFRLS algorithm is the best among the three, followed by the multiple new recursive least square algorithm with limited memory, and the RLS algorithm is the worst.

[0131] The MAE value and the RMSE value of the terminal voltage corresponding to the FFRLS algorithm are the smallest, which are 0.6 mV and 2.4 mV, respectively. The multiple new recursive least square algorithm with limited memory is second, which are 2.7 mV and 4.9 mV, respectively. The RLS algorithm is last, which are 5.6 mV and 8.1 mV, respectively.

[0132] Under the DST cycle, the multiple new recursive least square algorithm with limited memory introduced in the present application has better tracking performance than the RLS algorithm, and can better track the battery voltage. The MAE value and the 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 result can gradually and stably converge near the reference value. Figure 8 is the identification result graph of the ohmic resistance R0 under the DST cycle provided by an embodiment of the present application, Figure 9 is a comparison graph of the simulated terminal voltage and the actual terminal voltage under the DST cycle provided by an embodiment of the present application, Figure 10 is a terminal voltage error graph under the DST cycle provided by an embodiment of the present application.

[0133] The scheme provided in the application introduces the multiple innovation theory in the limited memory recursive least square 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 square algorithm is used to limit the memory length of the identification model; based on the identification model, the parameter identification of the second-order RC battery model is performed, the algorithm has better tracking performance than the RLS algorithm, has better stability than the FFRLS algorithm, and the identification result can quickly approach and stably converge to the vicinity of the reference value. Using the algorithm, a better parameter identification result can be obtained; because the SOC of the battery cannot be directly measured by an instrument, it is usually necessary to establish a battery model and estimate it by 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 cruising range of the electric vehicle, help 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 being overcharged or overdischarged, 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 resource waste.

[0134] Figure 11 A structural block diagram of a battery model parameter identification device provided by an embodiment of the application is shown in FIG. 11. For ease of illustration, only parts related to the embodiments of the application are shown. Figure 11 The battery model parameter identification device 1100 includes a construction module 1101 and an identification module 1102.

[0135] In an implementation manner, the device 1100 can be used to implement the method shown in Figure 1 For example, the construction module 1101 is configured to implement S110, and the identification module 1102 is configured to implement S120.

[0136] The scheme provided in the application introduces the multi-innovation theory in the recursive least square algorithm with limited memory to determine the identification model, the multi-innovation theory is used to obtain innovation length data as the input of the identification model, and the recursive least square 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 parameters of the second-order RC battery model are identified, the algorithm has better tracking than the RLS algorithm, has better stability than the FFRLS algorithm, and the identification result can quickly approach and stably converge near the reference value. Using the algorithm can obtain a better parameter identification result; because the SOC of the battery cannot be directly measured by an instrument, it is usually necessary to estimate it by establishing a 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 cruising range of the electric vehicle, help the driver make relevant preparations in advance, and avoid vehicle failure due to insufficient power; accurate SOC estimation can also prevent the battery from being overcharged or overdischarged, 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 resource waste.

[0137] Figure 12 The structural schematic diagram of the electronic device provided by an embodiment of the application is shown in the figure. Figure 12 As shown in the figure, the electronic device 12 of the embodiment includes at least one processor 120 (only one processor is shown in the figure), a memory 121 and a computer program 122 stored in the memory 121 and executable on the at least one processor 120, and the processor 120 implements the steps in any method embodiment described above when executing the computer program 122. Figure 12 The electronic device 12 can be a desktop computer, a notebook computer, a palm computer and a cloud server, etc. The electronic device can include, but is not limited to, the processor 120 and the memory 121. Those skilled in the art can understand that

[0138] The electronic device 12 shown in the figure is only an example and does not constitute a limitation on the electronic device 12, and can include more or fewer components than shown in the figure, or combine certain components or different components, for example, can also include input / output devices, network access devices, etc. Figure 12

[0139] ​The processor 120 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

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

[0141] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to complete all or part of the above-described functions. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or in the form of software functional unit. In addition, the specific names of each functional unit and module are only for convenient distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0142] The electronic device includes at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, and the processor implements the steps in any of the above method embodiments when executing the computer program.

[0143] The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps in any of the above method embodiments.

[0144] The computer program product, when executed on a mobile terminal, causes the mobile terminal to implement the steps in any of the above method embodiments.

[0145] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such understanding, the present application implements all or part of the processes in the above embodiments, which can be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program, when executed by a processor, can implement the steps in any of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms. The computer-readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. For example, a U disk, a mobile hard disk, a magnetic disk or an optical disk, etc.

[0146] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0147] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0148] In the embodiments of the present application, it should be understood that the disclosed apparatus / equipment and method can be implemented in other manners. For example, the embodiments of the apparatus / equipment described above are merely illustrative. For example, the division of the modules or units is merely logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between the units can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0149] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place or distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0150] The above-described embodiments are merely used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for identifying battery model parameters, characterized in that, The method includes: In the recursive least squares algorithm with limited memory, multiple innovation theory is introduced to determine the identification model. The multiple innovation theory is used to obtain innovation 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. The recursive least squares algorithm for limited memory is used to limit the memory length of the identification model, including: obtaining the input observation matrix and 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 output observation matrix based on the input observation matrix and output observation matrix; determining the expansion of the covariance matrix based on the covariance matrix and the matrix inversion operation formula; and determining the recursive least squares algorithm for limited memory based on the expansion of the covariance matrix and the time k to be eliminated. Introducing multiple innovation theory into the recursive least squares algorithm with limited memory, the identification model is determined by: determining the multiple innovation input vector, multiple innovation output vector, and multiple innovation error vector based on the preset multiple innovation length p, the time k to be eliminated, the multiple innovation theory, and parameter estimates; determining the initial identification model based on the multiple innovation output vector and the recursive least squares update formula; and determining the identification model based on the initial identification model, the time k to be eliminated, and the matrix inversion operation formula. Based on the identification model, real-time voltage, and real-time current, parameter identification is performed on the second-order RC battery model.

2. The method according to claim 1, characterized in that, Before acquiring the innovation length data, a preset multiple innovation length p needs to be determined. The method for determining the preset multiple innovation length p includes: simulating a first preset operating condition and a second preset operating condition based on preset simulation software, wherein the first preset operating condition and the second preset operating condition are used to determine the identification data of the second-order RC battery model line parameters; determining the calculation result of the identification data of the second-order RC battery model line parameters by using a preset step length; and determining the preset multiple innovation length p based on the calculation result.

3. 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, battery experiments are required. The battery experiments include testing the usable capacity of the battery, HPPC cycle test, FUDS cycle test, and DST cycle test.

4. The method according to claim 2, characterized in that, The preset simulation software is Matlab simulation software, the first preset working condition is the urban driving working condition, and the second preset working condition is the dynamic stress test working condition.

5. The method according to claim 3, characterized in that, Before conducting battery experiments, a battery testing experimental platform needs to be set up. The battery testing experimental platform includes a host computer, a battery detection system, and a constant temperature chamber with a temperature of 25°C.

6. A battery model parameter identification device, characterized in that, The apparatus is used to perform the battery model parameter identification method according to any one of claims 1-5, the apparatus comprising: A construction module is used to introduce multiple innovation theory into the recursive least squares algorithm with limited memory to determine the identification model. The multiple innovation theory is used to obtain innovation length data as input to 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 identify parameters of the second-order RC battery model based on the identification model, real-time voltage, and real-time current.

7. 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, it implements the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5.

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