Parameter identification and actual capacity estimation method for lithium-ion battery based on constant voltage charging current decoupling dynamic characteristics

Abstract

The application discloses a lithium ion battery parameter identification and actual capacity estimation method based on constant voltage charging current decoupling dynamic characteristics, which comprises the following steps: obtaining a battery constant voltage charging current curve offline; identifying model parameters; determining characteristic variables; establishing the correlation between the characteristic variables and the actual capacity of the battery; measuring and recording the current value of the battery constant voltage charging in real time; identifying the characteristic variables after the end of the charging process; and estimating the actual capacity of the battery according to the characteristic variables. The estimation method has the following three advantages: (1) compared with the traditional parameter identification method applied to the constant voltage charging working condition, the parameter identification method provided by the application is simple to perform and has low computational complexity; (2) the characteristic parameters selected by the application can more accurately and effectively represent the capacity attenuation of the battery; and (3) compared with the battery actual capacity estimation method based on dynamic discharge data and constant current charging data, the estimation method provided by the application has high robustness.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery, in particular to a lithium ion battery parameter identification and actual capacity estimation method based on constant voltage charging current decoupling dynamic characteristics. BACKGROUND

[0002] Power battery, as one of the core technologies of new energy vehicles, is the heart of electric new energy vehicles. Due to the advantages of high energy density, high power density and no memory effect, lithium ion power battery has been widely used in vehicle energy storage system. In order to ensure the safe and reliable operation of the vehicle power battery system, a set of efficient battery management system is needed to supervise and optimize it in real time, and the fast and accurate identification of battery parameters and actual capacity is one of the core technologies of battery management.

[0003] Compared with dynamic driving conditions, the current excitation characteristics of the vehicle battery energy storage system under charging conditions are relatively simple, and the measurement data collected under this condition contains more information reflecting the battery state of health, especially the capacity decay state. Therefore, the lithium ion battery parameter identification and actual capacity estimation method based on charging data has attracted widespread attention. SUMMARY

[0004] In order to solve the above problems, the present application provides a lithium ion battery parameter identification and actual capacity estimation method based on constant voltage charging current decoupling dynamic characteristics. The estimation method accurately estimates the equivalent circuit model and actual capacity of the battery according to the dynamic characteristics of the constant voltage charging current decoupling component after the end of the battery constant voltage charging process, so as to realize the efficient and safe operation of the battery system.

[0005] The present application is realized by the following technical scheme.

[0006] The present application provides a lithium ion battery parameter identification and actual capacity estimation method based on constant voltage charging current decoupling dynamic characteristics, which comprises the following steps:

[0007] S1, obtaining the constant voltage charging stage current curve of the battery under different state of health according to the aging test data;

[0008] S2, identifying the n-order resistance-inductance network equivalent circuit model parameters according to the constant voltage charging stage current curve of the battery;

[0009] S3, analyzing the correlation between the time constant of each resistance-inductance network and the battery capacity, and determining the characteristic variable representing the capacity decay of the battery;

[0010] S4, establishing the correlation between the characteristic variable determined in step S3 and the actual capacity of the battery;

[0011] S5, when the battery enters the constant voltage charging control mode, the charging current value of the battery is measured and recorded;

[0012] S6, when the charging process is completed, the characteristic variable determined in step S3 is identified;

[0013] S7, the characteristic variable obtained in step S6 is substituted into the correlation relationship established in step S4 to estimate the actual capacity value of the battery.

[0014] In step S2, the simplified current expression of the n-order resistance-inductance network equivalent circuit model is:

[0015]

[0016] wherein t i is the constant voltage charging time, and t1=0 represents the initial time when the battery enters the constant voltage charging control mode, t i+1 -t i represents the sampling period, N represents the size of the recorded data, I(t i ) represents the constant voltage charging current of the battery, I k (t i ) and τ k represent the branch current and time constant of the k-order resistance-inductance network, respectively, and satisfy τ m <τ n , wherein m

[0017] The parameter identification method of the n-order resistance-inductance network equivalent circuit model in step S2 includes the following steps:

[0018] S2.1, assigning the parameter k to the model order n;

[0019] S2.2, determining the decoupling critical time t i,k according to the current dynamic characteristics;

[0020] S2.3, identifying the k-order model parameters;

[0021] S2.4, subtracting the estimated k-order model generated current curve from the measured current;

[0022] S2.5, reducing the value of parameter k by 1;

[0023] S2.6, determining whether the value of k is 1, if greater than 1, executing step S2.2, otherwise executing step S2.7;

[0024] S2.7, identifying the first-order model parameters.

[0025] In step S3, the correlation relationship expression is:

[0026]

[0027] Cap est is the estimated actual capacity value, a j,i is the coefficient of the correlation relationship, l is the order of the correlation relationship, j is the number of segments of the piecewise function, τ k,1,j and τ k,2,j are respectively the lower limit value and the upper limit value of the interval defined by the jth segment of the function.

[0028] In the step 2.3, the kth order model parameter includes the corresponding time constant τ k and the initial current value I k (0), wherein k>1, and the expressions for estimating the time constant and the initial current of the kth order model are as follows:

[0029]

[0030] wherein I k,est (0) is the initial current value of the kth order model identified, τ k,est is the time constant of the kth order model identified, and I d,k is the current generated by the decoupled kth order model, and the expression is as follows:

[0031]

[0032] wherein p is the order corresponding to the identified model; and I p,est is the current generated by the identified model.

[0033] In the step 2.4, the expression of the current generated by the kth order model is as follows:

[0034]

[0035] In the step 2.7, the first order model parameter includes the corresponding time constant τ1 and the initial current value I1(0), and the expressions are as follows:

[0036]

[0037] wherein t i,1 is the decoupling critical time of the current generated by the first order model, I 1,est (t i,1 ) is the current value of the first order model identified at the decoupling critical time, I 1,est (0) is the initial current value of the first order model identified, and τ 1,est is the time constant of the first order model identified.

[0038] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0039] 1. Compared with the traditional parameter identification method applied to the constant voltage charging working condition, the parameter identification method provided by the application is simple to perform and has low operation amount;

[0040] 2. The characteristic parameters used in the application can more accurately and effectively represent the capacity attenuation of the battery;

[0041] 3. Compared with the battery actual capacity estimation method based on dynamic discharge data and constant current charging data, the estimation method provided by the application has higher robustness. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 It is a flowchart of the battery actual capacity estimation method of the application.

[0043] Figure 2 It is a flowchart of the battery model parameter identification under the constant voltage charging working condition.

[0044] Figure 3 It is a comparison between the battery model output results obtained based on the parameter identification method of the application and the actually measured constant voltage charging current waveforms under different state-of-health (SoH).

[0045] Figure 4 It is a comparison between the battery capacity value results obtained by using the application and the actually measured battery capacity values. DETAILED DESCRIPTION

[0046] Exemplary embodiments of the application will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the application are shown in the drawings, it should be understood that the application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the application can be more thoroughly understood and so that the scope of the application can be fully conveyed to those skilled in the art.

[0047] The embodiment of the application provides a lithium ion battery parameter identification and actual capacity estimation method based on constant voltage charging current decoupling dynamic characteristics, and realizes efficient and safe management of a vehicle-mounted lithium ion battery energy storage system.

[0048] A lithium ion battery parameter identification and actual capacity estimation method based on constant voltage charging current decoupling dynamic characteristics is shown in the flowchart as Figure 1 The method mainly includes two parts; the first part is an offline analysis part, and the second part is an online estimation part; the two parts will be further described below.

[0049] The offline analysis part includes the following steps:

[0050] 1) According to the aging test data, the constant voltage charging stage current curve of the battery at different health states is obtained;

[0051] 2) According to the constant voltage charging stage current curve of the battery, the n-order resistance-inductance network equivalent circuit model parameters are identified, as shown in the formula (2); Figure 2

[0052] The simplified current expression of the n-order resistance-inductance network equivalent circuit model is:

[0053]

[0054] Wherein, t i is the constant voltage charging time, and t1=0 represents the initial time when the battery enters the constant voltage charging control mode, t i+1 -t i represents the sampling period, N represents the recorded data size, I(t i ) represents the constant voltage charging current of the battery, I k (t i ) and τ k represent the branch current and time constant of the k-order resistance-inductance network respectively, and satisfy τ m <τ n , wherein, m<n.

[0055] Step 2.1), the parameter k is assigned as the model order n;

[0056] Step 2.2), according to the current dynamic characteristics, the decoupling critical time t i,k is determined;

[0057] Step 2.3), the time constant and initial current value of the k-order model are identified:

[0058]

[0059] Wherein, I k,est (0) is the initial current value of the identified k-order model, τ k,est is the time constant of the identified k-order model, and I d,k is the current generated by the decoupled k-order model, and its expression is:

[0060]

[0061] Wherein, p is the order corresponding to the identified model; I p,est is the current generated by the identified model. Step 2.4), the estimated k-order model current curve is subtracted from the measured current, wherein the k-order model current expression is:

[0062] ​

[0063] Step 2.5), reduce the value of parameter k by 1;

[0064] Step 2.6), determine whether the value of k is 1, if greater than 1, execute Step 2.2), otherwise execute Step 2.7);

[0065] Step 2.7), identify the time constant and initial current value of the first-order model:

[0066]

[0067] wherein, t i,1 is the decoupling critical time of the first-order model, I 1,est (t i,1 ) is the current value of the identified first-order model at the decoupling critical time, I 1,est (0) is the initial current value of the identified first-order model, τ 1,est is the time constant of the identified first-order model.

[0068] 3) Perform correlation analysis of the time constant of each resistance-inductance network and the battery capacity to determine the characteristic variable representing the battery capacity decay;

[0069] 4) Establish the correlation relationship between the characteristic variable determined in Step 3 and the actual capacity of the battery:

[0070]

[0071] The online estimation part includes the following steps:

[0072] 1) When the battery enters the constant voltage charging control mode, measure and record the charging current value of the battery;

[0073] 2) After the charging process is completed, identify the characteristic variable determined in Step 3) of the offline analysis; wherein the identification method is the same as that in Step 2) of the offline analysis;

[0074] 3) Substitute the characteristic variable obtained in Step 2) of the online estimation into the correlation relationship established in Step 4) of the offline analysis to estimate the actual capacity value of the battery.

[0075] In this embodiment, the implementation object is a ternary lithium-ion battery with a nominal capacity of 4.8 Ah, and the actual application is not limited thereto. The aging test is performed at room temperature, and the model order is set to 2. The battery model output results obtained based on the parameter identification method of the present application are compared with the actual measured constant voltage charging current waveform as shown in Figure 3As shown, it can be found that the model output current curve is very close to the measured current curve. The battery capacity value estimated by the present application is compared with the actual measured battery capacity value by taking τ2 as the characteristic variable, as shown in the table. Figure 4 As shown, it can be found that the estimated battery capacity value can track the measured value well, and thus the present method can well realize online estimation of the actual battery capacity.

[0076] In summary, the present application provides a lithium ion battery parameter identification and actual capacity estimation method based on constant voltage charging current decoupling dynamic characteristics, which comprises: offline obtaining a battery constant voltage charging current curve; identifying model parameters; determining a characteristic variable; establishing a correlation relationship between the characteristic variable and the actual capacity of the battery; measuring and recording the current value of the battery constant voltage charging in real time; identifying the characteristic variable after the charging process is completed; and estimating the actual capacity of the battery according to the characteristic variable. The estimation method provided by the present application has the following three advantages: (1) compared with the traditional parameter identification method applied to the constant voltage charging working condition, the parameter identification method provided by the present application is simple to execute and has low computational complexity; (2) the characteristic parameter selected by the present application can more accurately and effectively represent the capacity attenuation of the battery; and (3) compared with the battery actual capacity estimation method based on dynamic discharge data and constant current charging data, the estimation method provided by the present application has high robustness.

[0077] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of a preferred embodiment, and the above-mentioned embodiment numbers are only for description and do not represent the advantages and disadvantages of the embodiments.

[0078] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "exemplary embodiment", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the exemplary description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0079] Although the embodiments of the present application have been shown and described, those skilled in the art can understand that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and purposes of the present application, and the scope of the present application is defined by the claims and their equivalents.

Claims

1. A method for identifying lithium-ion battery parameters and estimating actual capacity based on the decoupling dynamic characteristics of constant voltage charging current, characterized in that, The method includes the following steps: S1. Based on the aging test data, obtain the constant voltage charging current curve of the battery under different health conditions. S2. Based on the current curve of the constant voltage charging stage of the battery, identify the parameters of the equivalent circuit model of the nth-order resistor-inductor network. S3. Perform a correlation analysis between the time constant of each resistor-inductor network and the battery capacity to determine the characteristic variables that characterize the battery capacity decay. S4, Establish the correlation between the characteristic variables determined in step S3 and the actual battery capacity; S5, after the battery enters the constant voltage charging control mode, measure and record the charging current value of the battery. S6. After the charging process is completed, the characteristic variables determined in step S3 are identified. S7. Substitute the feature variables obtained in step S6 into the correlation relationship established in step S4 to estimate the actual capacity value of the battery. In step S2, the simplified current expression of the equivalent circuit model of the nth-order resistor-inductor network is: Among them, t i Let t1 = 0 represent the initial moment when the battery enters the constant voltage charging control mode. i+1 -t i I(t) represents the sampling period, N represents the size of the recorded data, and I(t) represents the data size. i I represents the constant voltage charging current of the battery. k (t i ) and τ k Let represent the branch current and time constant of the k-th order resistive-inductor network, respectively, and satisfy τ. m <τ n , where m <n; In step S2, the parameter identification method for the equivalent circuit model of the nth-order resistor-inductor network includes the following steps: S2.1, assign the parameter k to the model order n; S2.2, Determine the decoupling critical time t based on the current dynamic characteristics. i,k ; S2.3, Identify the parameters of the k-th order model; S2.4, subtract the estimated k-th order model from the measured current to generate the current curve; S2.5, decrease the value of parameter k by 1; S2.6, Determine if the value of k is 1. If it is greater than 1, proceed to step S2.2; otherwise, proceed to step S2.

7. S2.7, Identify the parameters of the first-order model.

2. The method for lithium-ion battery parameter identification and actual capacity estimation based on the decoupling dynamic characteristics of constant voltage charging current according to claim 1, characterized in that, In step S3, the correlation expression is: Among them, Cap est To estimate the actual capacity value, a j,i Let be the coefficient of the correlation, l be the order of the correlation, j be the number of segments in the piecewise function, and τ be the coefficient of the correlation. k,1,j and τ k,2,j These are the lower and upper limits of the function's defined interval for the j-th segment, respectively.

3. The method for lithium-ion battery parameter identification and actual capacity estimation based on the decoupling dynamic characteristics of constant voltage charging current according to claim 2, characterized in that... In step 2.3, the parameters of the k-th order model include the corresponding time constant τ. k and initial current value I k (0), where k>1.

4. The method for lithium-ion battery parameter identification and actual capacity estimation based on constant voltage charging current decoupling dynamic characteristics according to claim 3, characterized in that... In step 2.4, the current expression generated by the k-th order model is: Among them, I k,est (0) is the initial current value τ for identifying the obtained k-th order model. k,est To identify the time constant of the obtained k-th order model.

5. The method for lithium-ion battery parameter identification and actual capacity estimation based on constant voltage charging current decoupling dynamic characteristics according to claim 4, characterized in that... In step 2.7, the parameters of the first-order model include the corresponding time constant τ1 and the initial current value I1(0); the expression for estimating the time constant of the first-order model is: Among them, t i,1 For the first-order model, the critical time for decoupling current is I. 1,est (t i,1 To identify the current value of the obtained first-order model at the decoupling critical time, I 1,est (0) is the initial current value τ for identifying the obtained first-order model. 1,est To identify the time constant of the obtained first-order model.

6. The method for lithium-ion battery parameter identification and actual capacity estimation based on constant voltage charging current decoupling dynamic characteristics according to claim 5, characterized in that... The expression for estimating the time constant of the k-th order model is: Among them, I d,k To generate current for the decoupled k-th order model; the expression for generating current for the decoupled k-th order model is: Where p is the order of the identified model; I p,est The current generated for the identified model.

7. The method for lithium-ion battery parameter identification and actual capacity estimation based on constant voltage charging current decoupling dynamic characteristics according to claim 6, characterized in that... The expression for estimating the initial current of the k-th order model is:

8. The method for lithium-ion battery parameter identification and actual capacity estimation based on the decoupling dynamic characteristics of constant voltage charging current according to claim 7, characterized in that... The expression for estimating the initial current of the first-order model is:

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