Lithium-ion battery parameter and open circuit voltage identification method, system and medium

Abstract

The present invention provides a method, system and medium for identifying parameters and open circuit voltage of lithium-ion batteries, including: sensing the measurement data of the battery through a sensor, establishing a Thevenin battery equivalent circuit model according to the measurement data, and obtaining a terminal voltage equation; subtracting the terminal voltage equations at adjacent moments to obtain a parameter identification recursive formula that does not include the open circuit voltage, performing a bilinear transformation on the transfer function to obtain a parameter identification recursive formula that includes the open circuit voltage; performing parameter identification based on the parameter identification recursive formula that does not include the open circuit voltage to obtain a parameter identification value of the battery; based on the parameter identification value of the battery, substituting the parameter identification recursive formula that includes the open circuit voltage, and performing open circuit voltage identification by recursive least squares method. The present application uses a dual recursive least squares method with forgetting factor to identify the parameters and open circuit voltage of lithium-ion batteries respectively, which has good effectiveness and numerical stability, and solves the problem of numerical divergence of traditional identification methods.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion battery state prediction, and in particular, to a lithium-ion battery parameter and open-circuit voltage identification method system and medium, and more particularly, to a lithium-ion battery parameter and open-circuit voltage identification method and system based on a double-band forgetting factor recursive least squares method. Background Art

[0002] Lithium-ion batteries have high power density, high energy density, long cycle life and low cycle cost, and are widely used in electric vehicles, energy storage and other fields. During the long-term operation of lithium-ion batteries, they need to be modeled and evaluated to prevent risks such as overcharging, over-discharging and overheating. Among the commonly used models, the Thevenin model has the best compromise between accuracy and complexity. The current Thevenin battery model parameters are usually identified using the recursive least squares method and its derivative methods, combined with recursive formulas obtained based on transfer functions and other methods. The battery parameters should remain stable during its operation, but the traditional identification method has parameter divergence, which results in the inability to obtain stable values ​​for OCV identification.

[0003] Patent document CN112180259A (application number: CN202010852093.6) discloses a lithium-ion battery parameter fitting method based on genetic algorithm, including the following steps: modeling lithium-ion batteries; performing HPPC tests on lithium-ion batteries; using genetic algorithms to design objective functions, and setting weight factors of starting points and end points to obtain good fitting effects in the initial stage and stable stage; using genetic algorithm-based parameter fitting methods to fit experimental data; and identifying parameters of lithium-ion batteries according to the obtained fitting curve. However, this invention conducts a separate hybrid pulse power characteristic (HPPC, Hybrid Pulse Power Characteristic) test, and uses the test data obtained by this test at different states of charge (SOC, State of Charge) to calculate the parameters. The disadvantage of this type of offline parameter identification method is that it requires separate experiments and cannot obtain parameter values ​​corresponding to all SOCs. If the relationship between parameters and SOC is established by table lookup or fitting method, errors will be introduced. In addition, this method does not have the ability to obtain open circuit voltage values ​​for all SOCs.

[0004] Patent document CN110208707A (application number: CN201910517156.X) discloses a lithium-ion battery parameter estimation method based on an equivalent circuit model, establishing an equivalent circuit model of the lithium-ion battery to be tested - using the HPPC experimental data of the battery to be tested at different temperatures and different rates to identify the parameter ψ of the equivalent circuit model - using the calculation formula of the battery voltage, according to the query table of the equivalent circuit model parameter ψ, the required parameters of the battery to be tested at different temperatures and different states of charge are calculated. However, this invention performs a separate hybrid pulse power characteristic (HPPC, Hybrid Pulse Power Characteristic) test, and uses the test data obtained by this test at different states of charge (SOC, State of Charge) to calculate the parameters. The disadvantage of this type of offline parameter identification method is that it requires separate experiments and cannot obtain parameter values ​​corresponding to all SOCs. If the relationship between the parameters and SOC is established by using a lookup table method or a fitting method, errors will be introduced. In addition, this method does not have the ability to obtain open circuit voltage values ​​for all SOCs.

[0005] In the paper A novel approach to reconstruct open circuit voltage for state of charge estimation of lithium ion batteries in electric vehicles, a parameter identification recursive formula based on transfer function derivation is used for parameter identification. Although the parameters and open circuit voltage can be identified online in real time, it is different from the method in the present invention. Figure 5 (a), its open circuit voltage value diverges during the identification process. This paper proposes a reconstruction method of the open circuit voltage to suppress the numerical divergence. Although this method can obtain a relatively stable open circuit voltage identification value, this reconstruction method undoubtedly increases the workload in the later stage. In contrast, the present invention can achieve real-time online stable identification of parameters and open circuit voltage.

[0006] Patent document CN109085509A (application number: CN201811057543.1) discloses a parameter identification method and system for the relationship between the open circuit voltage and SOC of a lithium-ion battery. The method uses at least two different discharge rates to discharge the lithium battery at a constant current until the SOC is 0; the corresponding relationship between the open circuit voltage and the SOC is determined using the load voltage curves of each battery obtained. However, the invention cannot avoid mutual disturbance between parameters, and the parameters diverge when the parameters are identified with the traditional forgetting factor. Summary of the invention

[0007] In view of the defects in the prior art, the object of the present invention is to provide a method, system and medium for identifying parameters and open circuit voltage of a lithium-ion battery.

[0008] A method for identifying parameters and open circuit voltage of a lithium-ion battery provided by the present invention includes:

[0009] Step S1: sensing the measurement data of the battery through the sensor, establishing the Thevenin battery equivalent circuit model according to the measurement data, and obtaining the terminal voltage equation;

[0010] Step S2: subtract the terminal voltage equations at adjacent moments to obtain a parameter identification recursive formula that does not include the open-circuit voltage, and perform a bilinear transformation on the transfer function to obtain a parameter identification recursive formula that includes the open-circuit voltage;

[0011] Step S3: performing parameter identification based on a parameter identification recursive formula that does not include the open circuit voltage to obtain a parameter identification value of the battery;

[0012] Step S4: Based on the parameter identification value of the battery, the parameter identification recursive formula including the open circuit voltage is substituted and the open circuit voltage is identified by the recursive least square method.

[0013] Preferably, in step S1:

[0014] The terminal voltage equation is obtained using Kirchhoff's law. The battery terminal voltage equation is:

[0015]

[0016] Subscript t indicates battery port; k indicates step; U t，k The terminal voltage of the battery at step k; OCV k is the open circuit voltage of the battery at step k; I t，k is the current of the battery in step k; R 0，k is the ohmic internal resistance of the battery at step k; p represents the polarization reaction; U p，k is the polarization voltage of the kth step; Δt represents the time interval, which is equivalent to the step interval; τ k is the time constant of the kth step; U p，k-1 is the polarization voltage of the k-1th step; R p，k is the k-th step polarization internal resistance; I t，k-1 is the current at the k-1th step.

[0017] Preferably, in step S2:

[0018] The terminal voltage equations at adjacent moments are subtracted to obtain a parameter identification recursive formula 1 that does not include the open-circuit voltage. The transfer function is bilinearly transformed to obtain a parameter identification recursive formula 2 that includes the open-circuit voltage. The parameter identification recursive formula 1 and the parameter identification recursive formula 2 are in the form of:

[0019] y k =θ1y k-1 +θ2uk +θ3u k-1

[0020] Y k =OCV k

[0021] y k is the system output value corresponding to the recursive formula 1 of the k-th step parameter identification; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 of the k-th step parameter identification, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; y k-1 is the system output value corresponding to the recursive formula of the k-1th step parameter identification; u k u is the system input value corresponding to the recursive formula of the k-th step parameter identification; k-1 is the system input value corresponding to the recursive formula of the k-1th step parameter identification; Y k OCV is the system output value corresponding to the recursive formula 2 of the k-th step parameter identification; k is the parameter to be identified in the parameter identification recursive formula 2, and is the open circuit voltage of the battery in the kth step;

[0022] By subtracting the adjacent moments of equation (1), we can obtain:

[0023]

[0024] U t,k-1 is the voltage of the battery at step k-1; U t,k-2 is the voltage of the battery at step k-2; I t,k-2 is the current of the k-2th step; τ is the time constant of the battery;

[0025] After Laplace transforming equation (1), we get:

[0026]

[0027] U t (s) is the terminal voltage of the battery in the complex frequency domain; s is the operator in the complex frequency domain; OCV(s) is the open circuit voltage of the battery in the complex frequency domain; I t (s) is the current of the battery in the complex frequency domain; R0 is the ohmic internal resistance of the battery; R p is the polarization internal resistance of the battery;

[0028] Introducing bilinear transformation And after converting to time domain discrete equation, we get:

[0029]

[0030] z -1 is an operator in the z domain; I t,k is the current of the battery in step k; I t,k-1is the current of the battery in the k-1th step; a1, a2 and a3 are coefficients, which are the combination of the battery's ohmic resistance, internal resistance, polarization internal resistance and time constant.

[0031] Preferably, in step S3:

[0032] The first band forgetting factor recursive least square method is used to perform parameter identification to obtain the parameter identification value of the battery;

[0033] After converting equation (2) and equation (4) into vector form, we get:

[0034]

[0035] Y k =OCV k (6)

[0036] y k is the system output vector corresponding to the recursive formula of parameter identification at time k; θ k is the parameter vector to be identified in the recursive formula 1 for parameter identification at time k; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 for parameter identification at time k, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; is the system input vector corresponding to the parameter identification recursive formula, whose elements contain the input values;

[0037] Set the initial value of parameter identification θ0 = [0 0 0] T , OCV0=0;

[0038] θ0 is the initial value of the value to be identified in the parameter identification recursive formula 1; OCV0 is the initial value of the value to be identified in the parameter identification recursive formula 2.

[0039] The recursive least squares method with forgetting factor is:

[0040]

[0041]

[0042]

[0043] K k is the gain value of the kth step; P k-1 is the covariance matrix of the k-1th step; is the system input vector of the kth step; λ is the forgetting factor; θ k is the parameter to be identified in the kth step; θ k-1 is the parameter to be identified in the k-1th step; y k is the system output value of the kth step; P kis the covariance matrix of the kth step; I is the identity matrix of the same order as the covariance matrix.

[0044] Preferably, in step S4:

[0045] The open circuit voltage is identified by using the recursive least square method with the second band forgetting factor;

[0046] The first band forgetting factor recursive least squares method is used to identify the parameters of the battery, and a set of parameters is obtained as θ k =[θ1 θ2 θ3] T , combined with equation (2), the model parameter value can be obtained as:

[0047] R0=θ2 (10)

[0048]

[0049]

[0050] θ k is the parameter vector to be identified in the recursive formula 1 for parameter identification at time k; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 for parameter identification at time k, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; R0 is the ohmic internal resistance of the battery; R p is the polarization internal resistance of the battery; τ is the time constant of the battery;

[0051] The obtained parameter values ​​R0, R p , τ is substituted into equation (13) to equation (17) to obtain the identification value of OCV:

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] U t,k is the terminal voltage of the battery at step k; a1, a2, a3, a4 are coefficients, which are the combination of the battery's ohmic resistance, internal resistance, polarization resistance, and time constant; U t,k-1 is the terminal voltage of the battery at step k-1; I t,k is the current of the battery in step k; I t,k-1 is the current of the battery in the k-1th step.

[0058] A lithium-ion battery parameter and open circuit voltage identification system provided by the present invention comprises:

[0059] Module M1: senses the battery measurement data through the sensor, establishes the Thevenin battery equivalent circuit model based on the measurement data, and obtains the terminal voltage equation;

[0060] Module M2: Subtract the terminal voltage equations at adjacent moments to obtain the parameter identification recursive formula that does not include the open-circuit voltage, and perform bilinear transformation on the transfer function to obtain the parameter identification recursive formula that includes the open-circuit voltage;

[0061] Module M3: Perform parameter identification based on a parameter identification recursive formula that does not include the open circuit voltage to obtain a parameter identification value of the battery;

[0062] Module M4: Based on the parameter identification value of the battery, the parameter identification recursive formula including the open circuit voltage is substituted and the open circuit voltage is identified by the recursive least square method.

[0063] Preferably, in the module M1:

[0064] The terminal voltage equation is obtained using Kirchhoff's law. The battery terminal voltage equation is:

[0065]

[0066] Subscript t indicates battery port; k indicates step; U t，k The terminal voltage of the battery at step k; OCV k is the open circuit voltage of the battery at step k; I t，k is the current of the battery in step k; R 0，k is the ohmic internal resistance of the battery at step k; p represents the polarization reaction; U p，k is the polarization voltage of the kth step; Δt represents the time interval, which is equivalent to the step interval; τ k is the time constant of the kth step; U p，k-1 is the polarization voltage of the k-1th step; R p，k is the k-th step polarization internal resistance; I t，k-1 is the current at the k-1th step.

[0067] Preferably, in the module M2:

[0068] The terminal voltage equations at adjacent moments are subtracted to obtain a parameter identification recursive formula 1 that does not include the open-circuit voltage. The transfer function is bilinearly transformed to obtain a parameter identification recursive formula 2 that includes the open-circuit voltage. The parameter identification recursive formula 1 and the parameter identification recursive formula 2 are in the form of:

[0069] y k =θ1y k-1 +θ2u k +θ3u k-1

[0070] Y k =OCV k

[0071] y k is the system output value corresponding to the recursive formula 1 of the k-th step parameter identification; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 of the k-th step parameter identification, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; y k-1 is the system output value corresponding to the recursive formula of the k-1th step parameter identification; u k is the system input value corresponding to the recursive formula of the k-th step parameter identification; u k-1 is the system input value corresponding to the recursive formula of the k-1th step parameter identification; Y k OCV is the system output value corresponding to the recursive formula 2 of the k-th step parameter identification; k is the parameter to be identified in the parameter identification recursive formula 2, and is the open circuit voltage of the battery in the kth step;

[0072] By subtracting the adjacent moments of equation (1), we can obtain:

[0073]

[0074] U t,k-1 is the voltage of the battery at step k-1; U t,k-2 is the voltage of the battery at step k-2; I t,k-2 is the current of the k-2th step; τ is the time constant of the battery;

[0075] After Laplace transforming equation (1), we get:

[0076]

[0077] U t (s) is the terminal voltage of the battery in the complex frequency domain; s is the operator in the complex frequency domain; OCV(s) is the open circuit voltage of the battery in the complex frequency domain; I t (s) is the current of the battery in the complex frequency domain; R0 is the ohmic internal resistance of the battery; R p is the polarization internal resistance of the battery;

[0078] Introducing bilinear transformation And after converting to time domain discrete equation, we get:

[0079]

[0080] z -1 is an operator in the z domain; I t,k is the current of the battery in step k; I t,k-1is the current of the battery in the k-1th step; a1, a2 and a3 are coefficients, which are the combination of the battery's ohmic resistance, internal resistance, polarization internal resistance and time constant.

[0081] Preferably, in the module M3:

[0082] The first band forgetting factor recursive least square method is used to perform parameter identification to obtain the parameter identification value of the battery;

[0083] After converting equation (2) and equation (4) into vector form, we get:

[0084]

[0085] Y k =OCV k (6)

[0086] y k is the system output vector corresponding to the recursive formula of parameter identification at time k; θ k is the parameter vector to be identified in the recursive formula 1 for parameter identification at time k; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 for parameter identification at time k, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; is the system input vector corresponding to the parameter identification recursive formula, whose elements contain the input values;

[0087] Set the initial value of parameter identification θ0 = [0 0 0] T , OCV0=0;

[0088] θ0 is the initial value of the value to be identified in the parameter identification recursive formula 1; OCV0 is the initial value of the value to be identified in the parameter identification recursive formula 2.

[0089] The recursive least squares method with forgetting factor is:

[0090]

[0091]

[0092]

[0093] K k is the gain value of the kth step; P k-1 is the covariance matrix of the k-1th step; is the system input vector of the kth step; λ is the forgetting factor; θ k is the parameter to be identified in the kth step; θ k-1 is the parameter to be identified in the k-1th step; y k is the system output value of the kth step; P kis the covariance matrix of the kth step; I is the identity matrix of the same order as the covariance matrix.

[0094] Preferably, in the module M4:

[0095] The open circuit voltage is identified by using the recursive least square method with the second band forgetting factor;

[0096] The first band forgetting factor recursive least squares method is used to identify the parameters of the battery, and a set of parameters is obtained as θ k =[θ1 θ2 θ3] T , combined with equation (2), the model parameter value can be obtained as:

[0097] R0=θ2 (10)

[0098]

[0099]

[0100] θ k is the parameter vector to be identified in the recursive formula 1 for parameter identification at time k; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 for parameter identification at time k, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; R0 is the ohmic internal resistance of the battery; R p is the polarization internal resistance of the battery; τ is the time constant of the battery;

[0101] The obtained parameter values ​​R0, R p , τ is substituted into equation (13) to equation (17) to obtain the identification value of OCV:

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] U t,k is the terminal voltage of the battery at step k; a1, a2, a3, a4 are coefficients, which are the combination of the battery's ohmic resistance, internal resistance, polarization resistance, and time constant; U t,k-1 is the terminal voltage of the battery at step k-1; I t,k is the current of the battery in step k; I t,k-1 is the current of the battery in the k-1th step.

[0108] A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the computer program implements the steps of any of the methods described above, or includes any of the systems described above.

[0109] Compared with the prior art, the present invention has the following beneficial effects:

[0110] 1. The present invention uses a double recursive least square method with forgetting factor, and identifies the model parameters and the open circuit voltage OCV in sequence according to the parameter identification recursive formula obtained by the two methods, thereby avoiding mutual disturbance between the parameters and solving the parameter divergence problem when the traditional recursive least square method with forgetting factor is used to identify the parameters;

[0111] 2. The method of the present invention can quickly obtain an accurate and stable open circuit voltage curve during the long-term use of the battery to update the battery model and match the attenuation degree of the battery, thereby improving the accuracy of modeling and facilitating the state estimation of parameters;

[0112] 3. In practical engineering applications, a stable method for obtaining open-circuit voltage from dynamic working conditions is provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0113] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0114] Figure 1 It is the flow chart of the double FFRLS algorithm;

[0115] Figure 2 This is the Thevenin equivalent circuit model diagram of a lithium-ion battery;

[0116] Figure 3 Test current and voltage waveforms for UDDS working conditions;

[0117] Figure 4 This is a comparison chart of parameter identification results between the dual FFRLS algorithm and the traditional FFRLS algorithm;

[0118] Figure 5 This is a comparison chart of the open-circuit voltage identification results of the dual FFRLS algorithm and the traditional FFRLS algorithm. DETAILED DESCRIPTION

[0119] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0120] Embodiment 1:

[0121] According to a lithium-ion battery parameter and open circuit voltage identification method provided by the present invention, Figure 1-Figure 5 As shown, including:

[0122] Step S1: sensing the measurement data of the battery through the sensor, establishing the Thevenin battery equivalent circuit model according to the measurement data, and obtaining the terminal voltage equation;

[0123] Step S2: subtract the terminal voltage equations at adjacent moments to obtain a parameter identification recursive formula that does not include the open-circuit voltage, and perform a bilinear transformation on the transfer function to obtain a parameter identification recursive formula that includes the open-circuit voltage;

[0124] Step S3: performing parameter identification based on a parameter identification recursive formula that does not include the open circuit voltage to obtain a parameter identification value of the battery;

[0125] Step S4: Based on the parameter identification value of the battery, the parameter identification recursive formula including the open circuit voltage is substituted and the open circuit voltage is identified by the recursive least square method.

[0126] Specifically, in step S1:

[0127] The terminal voltage equation is obtained using Kirchhoff's law. The battery terminal voltage equation is:

[0128]

[0129] Subscript t indicates battery port; k indicates step; U t，k The terminal voltage of the battery at step k; OCV k is the open circuit voltage of the battery at step k; I t，k is the current of the battery in step k; R 0，k is the ohmic internal resistance of the battery at step k; p represents the polarization reaction; U p，k is the polarization voltage of the kth step; Δt represents the time interval, which is equivalent to the step interval; τ k is the time constant of the kth step; U p，k-1 is the polarization voltage of the k-1th step; R p，k is the k-th step polarization internal resistance; I t，k-1 is the current at the k-1th step.

[0130] Specifically, in step S2:

[0131] The terminal voltage equations at adjacent moments are subtracted to obtain a parameter identification recursive formula 1 that does not include the open-circuit voltage. The transfer function is bilinearly transformed to obtain a parameter identification recursive formula 2 that includes the open-circuit voltage. The parameter identification recursive formula 1 and the parameter identification recursive formula 2 are in the form of:

[0132] y k=θ1y k-1 +θ2u k +θ3u k-1

[0133] Y k =OCV k

[0134] y k is the system output value corresponding to the k-th step parameter identification recursive formula 1; θ1, θ2 and θ3 are the parameters to be identified in the k-th step parameter identification recursive formula 1, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; y k-1 is the system output value corresponding to the recursive formula of the k-1th step parameter identification; u k u is the system input value corresponding to the recursive formula of the k-th step parameter identification; k-1 is the system input value corresponding to the recursive formula of the k-1th step parameter identification; Y k OCV is the system output value corresponding to the recursive formula 2 of the k-th step parameter identification; k is the parameter to be identified in the parameter identification recursive formula 2, and is the open circuit voltage of the battery in the kth step;

[0135] By subtracting the adjacent moments of equation (1), we can obtain:

[0136]

[0137] U t,k-1 is the voltage of the battery at step k-1; U t,k-2 is the voltage of the battery at step k-2; I t,k-2 is the current of the k-2th step; τ is the time constant of the battery;

[0138] After Laplace transforming equation (1), we get:

[0139]

[0140] U t (s) is the terminal voltage of the battery in the complex frequency domain; s is the operator in the complex frequency domain; OCV(s) is the open circuit voltage of the battery in the complex frequency domain; I t (s) is the current of the battery in the complex frequency domain; R0 is the ohmic internal resistance of the battery; R p is the polarization internal resistance of the battery;

[0141] Introducing bilinear transformation And after converting to time domain discrete equation, we get:

[0142]

[0143] z -1 is an operator in the z domain; I t,kis the current of the battery in step k; I t,k-1 is the current of the battery in the k-1th step; a1, a2 and a3 are coefficients, which are the combination of the battery's ohmic resistance, internal resistance, polarization internal resistance and time constant.

[0144] Specifically, in step S3:

[0145] The first band forgetting factor recursive least square method is used to perform parameter identification to obtain the parameter identification value of the battery;

[0146] After converting equation (2) and equation (4) into vector form, we get:

[0147]

[0148] Y k =OCV k (6)

[0149] y k is the system output vector corresponding to the recursive formula of parameter identification at time k; θ k is the parameter vector to be identified in the recursive formula 1 for parameter identification at time k; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 for parameter identification at time k, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; is the system input vector corresponding to the parameter identification recursive formula, whose elements contain the input values;

[0150] Set the initial value of parameter identification θ0 = [0 0 0] T , OCV0=0;

[0151] θ0 is the initial value of the value to be identified in the parameter identification recursive formula 1; OCV0 is the initial value of the value to be identified in the parameter identification recursive formula 2.

[0152] The recursive least squares method with forgetting factor is:

[0153]

[0154]

[0155]

[0156] K k is the gain value of the kth step; P k-1 is the covariance matrix of the k-1th step; is the system input vector of the kth step; λ is the forgetting factor; θ k is the parameter to be identified in the kth step; θ k-1 is the parameter to be identified in the k-1th step; y k is the system output value of the kth step; P kis the covariance matrix of the kth step; I is the identity matrix of the same order as the covariance matrix.

[0157] Specifically, in step S4:

[0158] The open circuit voltage is identified by using the recursive least square method with the second band forgetting factor;

[0159] The first band forgetting factor recursive least squares method is used to identify the parameters of the battery, and a set of parameters is obtained as θ k =[θ1 θ2 θ3] T , combined with equation (2), the model parameter value can be obtained as:

[0160] R0=θ2 (10)

[0161]

[0162]

[0163] θ k is the parameter vector to be identified in the recursive formula 1 for parameter identification at time k; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 for parameter identification at time k, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; R0 is the ohmic internal resistance of the battery; R p is the polarization internal resistance of the battery; τ is the time constant of the battery;

[0164] The obtained parameter values ​​R0, R p , τ is substituted into equation (13) to equation (17) to obtain the identification value of OCV:

[0165]

[0166]

[0167]

[0168]

[0169]

[0170] U t,k is the terminal voltage of the battery at step k; a1, a2, a3, a4 are coefficients, which are the combination of the battery's ohmic resistance, internal resistance, polarization resistance, and time constant; U t,k-1 is the terminal voltage of the battery at step k-1; I t,k is the current of the battery in step k; I t,k-1 is the current of the battery in the k-1th step.

[0171] A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of any of the above methods.

[0172] The identification value is obtained in order to monitor the lithium battery parameters in real time online, because the safety of lithium batteries is the most important, and real-time monitoring and control are required to prevent abnormal battery operation, such as fire caused by excessive temperature.

[0173] Embodiment 2:

[0174] Embodiment 2 is a preferred example of Embodiment 1, and is used to illustrate the present invention in more detail.

[0175] Those skilled in the art may understand the lithium-ion battery parameter and open-circuit voltage identification method provided by the present invention as a specific implementation of a lithium-ion battery parameter and open-circuit voltage identification system, that is, the lithium-ion battery parameter and open-circuit voltage identification system may be implemented by executing the step flow of the lithium-ion battery parameter and open-circuit voltage identification method.

[0176] A lithium-ion battery parameter and open circuit voltage identification system provided by the present invention comprises:

[0177] Module M1: senses the battery measurement data through the sensor, establishes the Thevenin battery equivalent circuit model based on the measurement data, and obtains the terminal voltage equation;

[0178] Module M2: Subtract the terminal voltage equations at adjacent moments to obtain the parameter identification recursive formula that does not include the open-circuit voltage, and perform bilinear transformation on the transfer function to obtain the parameter identification recursive formula that includes the open-circuit voltage;

[0179] Module M3: Perform parameter identification based on a parameter identification recursive formula that does not include the open circuit voltage to obtain a parameter identification value of the battery;

[0180] Module M4: Based on the parameter identification value of the battery, the parameter identification recursive formula including the open circuit voltage is substituted and the open circuit voltage is identified by the recursive least square method.

[0181] Specifically, in the module M1:

[0182] The terminal voltage equation is obtained using Kirchhoff's law. The battery terminal voltage equation is:

[0183]

[0184] Subscript t indicates battery port; k indicates step; U t，k The terminal voltage of the battery at step k; OCV k is the open circuit voltage of the battery at step k; I t，k is the current of the battery in step k; R 0，kis the ohmic internal resistance of the battery at step k; p represents the polarization reaction; U p，k is the polarization voltage of the kth step; Δt represents the time interval, which is equivalent to the step interval; τ k is the time constant of the kth step; U p，k-1 is the polarization voltage of the k-1th step; R p，k is the k-th step polarization internal resistance; I t，k-1 is the current at the k-1th step.

[0185] Specifically, in the module M2:

[0186] The terminal voltage equations at adjacent moments are subtracted to obtain a parameter identification recursive formula 1 that does not include the open-circuit voltage. The transfer function is bilinearly transformed to obtain a parameter identification recursive formula 2 that includes the open-circuit voltage. The parameter identification recursive formula 1 and the parameter identification recursive formula 2 are in the form of:

[0187] y k =θ1y k-1 +θ2u k +θ3u k-1

[0188] Y k =OCV k

[0189] y k is the system output value corresponding to the k-th step parameter identification recursive formula 1; θ1, θ2 and θ3 are the parameters to be identified in the k-th step parameter identification recursive formula 1, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; y k-1 is the system output value corresponding to the recursive formula of the k-1th step parameter identification; u k u is the system input value corresponding to the recursive formula of the k-th step parameter identification; k-1 is the system input value corresponding to the recursive formula of the k-1th step parameter identification; Y k OCV is the system output value corresponding to the recursive formula 2 of the k-th step parameter identification; k is the parameter to be identified in the parameter identification recursive formula 2, and is the open circuit voltage of the battery in the kth step;

[0190] By subtracting the adjacent moments of equation (1), we can obtain:

[0191]

[0192] U t,k-1 is the voltage of the battery at step k-1; U t,k-2 is the voltage of the battery at step k-2; I t,k-2 is the current of the k-2th step; τ is the time constant of the battery;

[0193] After Laplace transforming equation (1), we get:

[0194]

[0195] U t (s) is the terminal voltage of the battery in the complex frequency domain; s is the operator in the complex frequency domain; OCV(s) is the open circuit voltage of the battery in the complex frequency domain; I t (s) is the current of the battery in the complex frequency domain; R0 is the ohmic internal resistance of the battery; R p is the polarization internal resistance of the battery;

[0196] Introducing bilinear transformation And after converting to time domain discrete equation, we get:

[0197]

[0198] z -1 is an operator in the z domain; I t,k is the current of the battery in step k; I t,k-1 is the current of the battery in the k-1th step; a1, a2 and a3 are coefficients, which are the combination of the battery's ohmic resistance, internal resistance, polarization internal resistance and time constant.

[0199] Specifically, in the module M3:

[0200] The first band forgetting factor recursive least square method is used to perform parameter identification to obtain the parameter identification value of the battery;

[0201] After converting equation (2) and equation (4) into vector form, we get:

[0202]

[0203] Y k =OCV k (6)

[0204] y k is the system output vector corresponding to the recursive formula of parameter identification at time k; θ k is the parameter vector to be identified in the recursive formula 1 for parameter identification at time k; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 for parameter identification at time k, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; is the system input vector corresponding to the parameter identification recursive formula, whose elements contain the input values;

[0205] Set the initial value of parameter identification θ0 = [0 0 0] T , OCV0=0;

[0206] θ0 is the initial value of the value to be identified in the parameter identification recursive formula 1; OCV0 is the initial value of the value to be identified in the parameter identification recursive formula 2.

[0207] The recursive least squares method with forgetting factor is:

[0208]

[0209]

[0210]

[0211] K k is the gain value of the kth step; P k-1 is the covariance matrix of the k-1th step; is the system input vector of the kth step; λ is the forgetting factor; θ k is the parameter to be identified in the kth step; θ k-1 is the parameter to be identified in the k-1th step; y k is the system output value of the kth step; P k is the covariance matrix of the kth step; I is the identity matrix of the same order as the covariance matrix.

[0212] Specifically, in the module M4:

[0213] The open circuit voltage is identified by using the recursive least square method with the second band forgetting factor;

[0214] The first band forgetting factor recursive least squares method is used to identify the parameters of the battery, and a set of parameters is obtained as θ k =[θ1 θ2 θ3] T , combined with equation (2), the model parameter value can be obtained as:

[0215] R0=θ2 (10)

[0216]

[0217]

[0218] θ k is the parameter vector to be identified in the recursive formula 1 for parameter identification at time k; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 for parameter identification at time k, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; R0 is the ohmic internal resistance of the battery; R p is the polarization internal resistance of the battery; τ is the time constant of the battery;

[0219] The obtained parameter values ​​R0, R p , τ is substituted into equation (13) to equation (17) to obtain the identification value of OCV:

[0220]

[0221]

[0222]

[0223]

[0224]

[0225] U t,k is the terminal voltage of the battery at step k; a1, a2, a3, a4 are coefficients, which are the combination of the battery's ohmic resistance, internal resistance, polarization resistance, and time constant; U t,k-1 is the terminal voltage of the battery at step k-1; I t,k is the current of the battery in step k; I t,k-1 is the current of the battery in the k-1th step.

[0226] A computer-readable storage medium storing a computer program, wherein the computer program implements any of the above-mentioned systems when executed by a processor.

[0227] Embodiment 3:

[0228] Embodiment 3 is a preferred example of Embodiment 1, and is used to illustrate the present invention in more detail.

[0229] A method for identifying parameters and open circuit voltage of a lithium-ion battery based on a double recursive least squares method with a forgetting factor comprises the following steps:

[0230] Step 1, establish a Thevenin battery equivalent circuit model and use Kirchhoff's law to obtain its terminal voltage equation;

[0231] Step 2, subtract the terminal voltage equations at adjacent moments to obtain a parameter identification recursive formula 1 that does not include the open-circuit voltage, and perform a bilinear transformation on the transfer function to obtain a parameter identification recursive formula 2 that includes the open-circuit voltage;

[0232] Step 3, based on parameter identification recursive formula 1, use the first band forgetting factor recursive least square method to perform parameter identification to obtain the parameter identification value of the battery;

[0233] Step 4: Based on the parameter value obtained in step 3, substitute it into the parameter identification recursive formula 2 obtained in step 2 based on the transfer function, and use the second recursive least square method with forgetting factor to identify the open circuit voltage.

[0234] Specifically, in step 1, the battery terminal voltage equation is:

[0235]

[0236] Specifically, in step 2, the derivation of the parameter identification recursive formula 1 and the parameter identification recursive formula 2 is:

[0237] y k =θ1y k-1 +θ2u k +θ3u k-1 (2)

[0238] Y k =OCV k (3)

[0239] Specifically, in step 3, the recursive least square method with forgetting factor is:

[0240]

[0241]

[0242]

[0243] Specifically, the model parameters include R0, Rp, τ, and OCV.

[0244] Specifically, the model parameters satisfy

[0245] R0=θ2 (7)

[0246]

[0247]

[0248]

[0249]

[0250]

[0251]

[0252]

[0253] Specifically, the forgetting factor is set to 0.995.

[0254] Specifically, the parameters R0, Rp, and τ are first calculated according to the recursive formula 1, and then their values ​​are substituted into the recursive formula 2 to calculate the OCV.

[0255] Embodiment 4:

[0256] Embodiment 4 is a preferred example of Embodiment 1, and is used to illustrate the present invention in more detail.

[0257] The technical solution provided by the present invention is as follows:

[0258] A method for identifying parameters and open circuit voltage of a lithium-ion battery based on a double recursive least squares method with a forgetting factor comprises the following steps:

[0259] Step 1, establish a Thevenin battery equivalent circuit model and use Kirchhoff's law to obtain its terminal voltage equation;

[0260] Step 2, subtract the terminal voltage equations at adjacent moments to obtain a parameter identification recursive formula 1 that does not include the open-circuit voltage, and perform a bilinear transformation on the transfer function to obtain a parameter identification recursive formula 2 that includes the open-circuit voltage;

[0261] Step 3, based on parameter identification recursive formula 1, use the first band forgetting factor recursive least square method to perform parameter identification to obtain the parameter identification value of the battery;

[0262] Step 4: Based on the parameter value obtained in step 3, substitute it into the parameter identification recursive formula 2 obtained in step 2 based on the transfer function, and use the second recursive least square method with forgetting factor to identify the open circuit voltage.

[0263] In step 1, the terminal voltage equation of the Thevenin battery equivalent circuit model is:

[0264]

[0265] In step 2, equation (1) is subtracted at adjacent moments to obtain:

[0266]

[0267] In step 2, equation (1) is Laplace transformed to obtain:

[0268]

[0269] In step 2, the bilinear transformation is introduced And after converting to time domain discrete equation, we get:

[0270]

[0271] In step 3, equations (2) and (4) are converted into vector form to obtain:

[0272]

[0273] Y k =OCV k (6)

[0274] In step 3, set the initial parameter identification values ​​θ0 = [0 0 0]T and Y0 = 0.

[0275] In steps 3 and 4, the recursive least squares method with forgetting factor is specifically:

[0276]

[0277]

[0278]

[0279] Among them, k represents the step of iterative calculation, φ is the input vector of the identification algorithm, θ is the parameter vector of the generation identification, Kk is the gain matrix, and Pk is the covariance matrix.

[0280] In step 4, a set of model parameters identified in step 3 are substituted into equation (6) to perform OCV identification. The specific steps are as follows:

[0281] Assume that the identified parameters are θk = [θ1, k θ2, k θ3, k]T. Combining equation (2), we can get the model parameter values ​​as follows:

[0282] R0=θ2 (10)

[0283]

[0284]

[0285] In step 4, the identified parameter values ​​R0, Rp, τ are substituted into equations (13) to (17) to obtain the identified value of OCV.

[0286]

[0287]

[0288]

[0289]

[0290]

[0291] Embodiment 5:

[0292] Embodiment 5 is a preferred example of Embodiment 1, and is used to illustrate the present invention in more detail.

[0293] like Figure 1 As shown, the present invention provides a battery parameter and open circuit voltage identification method based on a dual-band forgetting factor recursive least squares method, and the steps are as follows:

[0294] Step 1, sensing the measurement data of the battery through sensors, including Ut,k, Ut,k-1, Ut,k-2, It,k, It,k-1, It,k-2;

[0295] Step 2, calculate parameter identification recursive equations 1 and 2 according to equations (2) to (6);

[0296] Step 3, calculate the gain matrix, covariance matrix and update parameter identification matrix according to equations (7), (8) and (9);

[0297] Step 4: According to the updated parameter identification matrix θk, substitute its values ​​into equations (10)-(17) to solve the model parameters, including R0, Rp, τ, and OCV.

[0298] The parameters and OCV are obtained according to the battery parameter and open circuit voltage identification method based on the double-band forgetting factor recursive least squares method.

[0299] In the traditional method of simultaneously identifying model parameters and OCV based on the recursive least squares method with forgetting factor, the parameters are prone to divergence due to the mutual influence between the parameters, which is particularly obvious at the beginning and end of the identification, especially at the end, the divergence of the parameters is very serious. In the parameter recursive formula 1, this method eliminates the influence of the open circuit voltage OCV on the parameters by making a difference between the terminal voltages at adjacent moments, and can obtain more stable model parameter identification values.

[0300] The OCV obtained by the traditional recursive least squares method with forgetting factor also has serious divergence in the early and late stages of identification. This method substitutes the stable model parameter values ​​that have been identified into the parameter identification recursive formula 2 for OCV identification, which can obtain a more stable OCV value.

[0301] Those skilled in the art know that, in addition to implementing the system, device and its various modules provided by the present invention in a purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers and embedded microcontrollers by logically programming the method steps. Therefore, the system, device and its various modules provided by the present invention can be considered as a hardware component, and the modules included therein for implementing various programs can also be considered as structures within the hardware component; the modules for implementing various functions can also be considered as both software programs for implementing the method and structures within the hardware component.

[0302] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for identifying parameters and open circuit voltage of a lithium-ion battery, characterized in that: include: Step S1: sensing the measurement data of the battery through the sensor, establishing the Thevenin battery equivalent circuit model according to the measurement data, and obtaining the terminal voltage equation; Step S2: subtract the terminal voltage equations at adjacent moments to obtain a parameter identification recursive formula that does not include the open-circuit voltage, and perform a bilinear transformation on the transfer function to obtain a parameter identification recursive formula that includes the open-circuit voltage; Step S3: performing parameter identification based on a parameter identification recursive formula that does not include the open circuit voltage to obtain a parameter identification value of the battery; Step S4: Based on the parameter identification value of the battery, substitute the parameter identification recursive formula including the open circuit voltage, and perform open circuit voltage identification by recursive least square method; In step S2: The terminal voltage equations at adjacent moments are subtracted to obtain a parameter identification recursive formula 1 that does not include the open-circuit voltage. The transfer function is bilinearly transformed to obtain a parameter identification recursive formula 2 that includes the open-circuit voltage. The parameter identification recursive formula 1 and the parameter identification recursive formula 2 are in the form of: y k =θ1y k-1 +θ2u k +θ3u k-1 Y k =OCV k y k is the system output value corresponding to the recursive formula 1 of the k-th step parameter identification; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 of the k-th step parameter identification, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; y k-1 is the system output value corresponding to the recursive formula of the k-1th step parameter identification; u k is the system input value corresponding to the recursive formula of the k-th step parameter identification; u k-1 is the system input value corresponding to the recursive formula of the k-1th step parameter identification; Y k OCV is the system output value corresponding to the recursive formula 2 of the k-th step parameter identification; k is the parameter to be identified in the parameter identification recursive formula 2, and is the open circuit voltage of the battery in the kth step; By subtracting the adjacent moments of equation (1), we can obtain: U t,k-1 is the voltage of the battery at step k-1; U t,k-2 is the voltage of the battery at step k-2; I t,k-2 is the current of the k-2th step; τ is the time constant of the battery; After Laplace transforming equation (1), we get: U t (s) is the terminal voltage of the battery in the complex frequency domain; s is the operator in the complex frequency domain; OCV(s) is the open circuit voltage of the battery in the complex frequency domain; I t (s) is the current of the battery in the complex frequency domain; R0 is the ohmic internal resistance of the battery; R p is the polarization internal resistance of the battery; Introducing bilinear transformation And after converting to time domain discrete equation, we get: z -1 is an operator in the z domain; I t,k is the current of the battery in step k; I t,k-1 is the current of the battery in the k-1th step; a1, a2 and a3 are coefficients, which are the combination of the battery's ohmic resistance, internal resistance, polarization internal resistance and time constant.

2. The method for identifying lithium-ion battery parameters and open circuit voltage according to claim 1, characterized in that: In step S1: The terminal voltage equation is obtained using Kirchhoff's law. The battery terminal voltage equation is: Subscript t indicates battery port; k indicates step; U t,k The terminal voltage of the battery at step k; OCV k is the open circuit voltage of the battery at step k; I t,k is the current of the battery in step k; R 0，k is the ohmic internal resistance of the battery at step k; p represents the polarization reaction; Up, k is the polarization voltage at step k; Δt represents the time interval, which is equivalent to the step interval; τ k is the time constant of the kth step; U p，k-1 is the polarization voltage of the k-1th step; R p，k is the k-th step polarization internal resistance; I t，k-1 is the current at the k-1th step.

3. The method for identifying parameters and open circuit voltage of a lithium-ion battery according to claim 1, characterized in that: In step S3: The first band forgetting factor recursive least square method is used to perform parameter identification to obtain the parameter identification value of the battery; After converting equation (2) and equation (4) into vector form, we get: Y k =OCV k (6) y k is the system output vector corresponding to the recursive formula of parameter identification at time k; θ k is the parameter vector to be identified in the recursive formula 1 for parameter identification at time k; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 for parameter identification at time k, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; is the system input vector corresponding to the recursive formula 1 for parameter identification, whose elements include various input values; Set the initial value of parameter identification θ0 = [000] T , OCV0=0; θ0 is the initial value of the value to be identified in the parameter identification recursive formula 1; OCV0 is the initial value of the value to be identified in the parameter identification recursive formula 2; The recursive least squares method with forgetting factor is: K k is the gain value of the kth step; P k-1 is the covariance matrix of the k-1th step; is the system input vector of the kth step; λ is the forgetting factor; θ k is the parameter to be identified in the kth step; θ k-1 is the parameter to be identified in the k-1th step; y k is the system output value of the kth step; P k is the covariance matrix of the kth step; I is the identity matrix of the same order as the covariance matrix.

4. The method for identifying parameters and open circuit voltage of a lithium-ion battery according to claim 1, characterized in that: In step S4: The open circuit voltage is identified by using the recursive least square method with the second band forgetting factor; The first band forgetting factor recursive least squares method is used to identify the parameters of the battery, and a set of parameters is obtained as θ k =[θ1θ2θ3] T , combined with equation (2), the model parameter value can be obtained as: R0=θ2 (10) θ k is the parameter vector to be identified in the recursive formula 1 for parameter identification at time k; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 for parameter identification at time k, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; R0 is the ohmic internal resistance of the battery; R p is the polarization internal resistance of the battery; τ is the time constant of the battery; The obtained parameter values ​​R0, R p , τ is substituted into equation (13) to equation (17) to obtain the identification value of OCV: U t,k is the terminal voltage of the battery at step k; a1, a2, a3, a4 are coefficients, which are the combination of the battery's ohmic resistance, internal resistance, polarization resistance, and time constant; U t,k-1 is the terminal voltage of the battery at step k-1; I t,k is the current of the battery in step k; I t,k-1 is the current of the battery in the k-1th step.

5. A lithium-ion battery parameter and open circuit voltage identification system, characterized in that: include: Module M1: senses the battery measurement data through the sensor, establishes the Thevenin battery equivalent circuit model based on the measurement data, and obtains the terminal voltage equation; Module M2: Subtract the terminal voltage equations at adjacent moments to obtain the parameter identification recursive formula that does not include the open-circuit voltage, and perform bilinear transformation on the transfer function to obtain the parameter identification recursive formula that includes the open-circuit voltage; Module M3: Perform parameter identification based on a parameter identification recursive formula that does not include the open circuit voltage to obtain a parameter identification value of the battery; Module M4: Based on the parameter identification value of the battery, the parameter identification recursive formula including the open circuit voltage is substituted and the open circuit voltage is identified by the recursive least square method; In the module M2: The terminal voltage equations at adjacent moments are subtracted to obtain a parameter identification recursive formula 1 that does not include the open-circuit voltage. The transfer function is bilinearly transformed to obtain a parameter identification recursive formula 2 that includes the open-circuit voltage. The parameter identification recursive formula 1 and the parameter identification recursive formula 2 are in the form of: y k =θ1y k-1 +θ2u k +θ3u k-1 Y k =OCV k y k is the system output value corresponding to the recursive formula 1 of the k-th step parameter identification; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 of the k-th step parameter identification, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; y k-1 is the system output value corresponding to the recursive formula of the k-1th step parameter identification; u k is the system input value corresponding to the recursive formula of the k-th step parameter identification; u k-1 is the system input value corresponding to the recursive formula of the k-1th step parameter identification; Y k OCV is the system output value corresponding to the recursive formula 2 of the k-th step parameter identification; k is the parameter to be identified in the parameter identification recursive formula 2, and is the open circuit voltage of the battery in the kth step; By subtracting the adjacent moments of equation (1), we can obtain: U t,k-1 is the voltage of the battery at step k-1; U t,k-2 is the voltage of the battery at step k-2; I t,k-2 is the current of the k-2th step; τ is the time constant of the battery; After Laplace transforming equation (1), we get: U t (s) is the terminal voltage of the battery in the complex frequency domain; s is the operator in the complex frequency domain; OCV(s) is the open circuit voltage of the battery in the complex frequency domain; I t (s) is the current of the battery in the complex frequency domain; R0 is the ohmic internal resistance of the battery; R p is the polarization internal resistance of the battery; Introducing bilinear transformation And after converting to time domain discrete equation, we get: z -1 is an operator in the z domain; I t,k is the current of the battery in step k; I t,k-1 is the current of the battery in the k-1th step; a1, a2 and a3 are coefficients, which are the combination of the battery's ohmic resistance, internal resistance, polarization internal resistance and time constant.

6. The lithium-ion battery parameter and open circuit voltage identification system according to claim 5, characterized in that: In the module M1: The terminal voltage equation is obtained using Kirchhoff's law. The battery terminal voltage equation is: Subscript t indicates battery port; k indicates step; U t,k The terminal voltage of the battery at step k; OCV k is the open circuit voltage of the battery at step k; I t,k is the current of the battery in step k; R 0，k is the ohmic internal resistance of the battery at step k; p represents the polarization reaction; Up, k is the polarization voltage at step k; Δt represents the time interval, which is equivalent to the step interval; τ k is the time constant of the kth step; U p，k-1 is the k-1th step polarization voltage; R p，k is the k-th step polarization internal resistance; I t，k-1 is the current at the k-1th step.

7. The lithium-ion battery parameter and open circuit voltage identification system according to claim 5, characterized in that: In the module M3: The first band forgetting factor recursive least square method is used to perform parameter identification to obtain the parameter identification value of the battery; After converting equation (2) and equation (4) into vector form, we get: Y k =OCV k (6) y k is the system output vector corresponding to the recursive formula of parameter identification at time k; θ k is the parameter vector to be identified in the recursive formula 1 for parameter identification at time k; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 for parameter identification at time k, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; is the system input vector corresponding to the recursive formula 1 for parameter identification, whose elements include various input values; Set the initial value of parameter identification θ0 = [000] T , OCV0=0; θ0 is the initial value of the value to be identified in the parameter identification recursive formula 1; OCV0 is the initial value of the value to be identified in the parameter identification recursive formula 2; The recursive least squares method with forgetting factor is: K k is the gain value of the kth step; P k-1 is the covariance matrix of the k-1th step; is the system input vector of the kth step; λ is the forgetting factor; θ k is the parameter to be identified in the kth step; θ k-1 is the parameter to be identified in the k-1th step; y k is the system output value of the kth step; P k is the covariance matrix of the kth step; I is the identity matrix of the same order as the covariance matrix; In the module M4: The open circuit voltage is identified by using the recursive least square method with the second band forgetting factor; The first band forgetting factor recursive least squares method is used to identify the parameters of the battery, and a set of parameters is obtained as θ k =[θ1θ2θ3] T , combined with equation (2), the model parameter value can be obtained as: R0=θ2 (10) θ k is the parameter vector to be identified in the recursive formula 1 for parameter identification at time k; θ1, θ2 and θ3 are the parameters to be identified in the recursive formula 1 for parameter identification at time k, which are the combination of ohmic internal resistance, polarization internal resistance and time constant; R0 is the ohmic internal resistance of the battery; R p is the polarization internal resistance of the battery; τ is the time constant of the battery; The obtained parameter values ​​R0, R p , τ is substituted into equation (13) to equation (17) to obtain the identification value of OCV: U t,k is the terminal voltage of the battery at step k; a1, a2, a3, a4 are coefficients, which are the combination of the battery's ohmic resistance, internal resistance, polarization resistance, and time constant; U t,k-1 is the terminal voltage of the battery at step k-1; I t,k is the current of the battery in step k; I t,k-1 is the current of the battery in the k-1th step.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented, or the computer program includes the system according to any one of claims 5 to 7.

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