Lithium battery second-order RC equivalent circuit model parameter identification method based on environment temperature

Abstract

The invention relates to the technical field of lithium battery modeling and parameter identification, and particularly discloses a lithium battery second-order RC equivalent circuit model parameter identification method based on environment temperature, which comprises the following steps: S1, carrying out pulse charging and discharging tests at different environment temperatures to obtain corresponding battery experiment data; s2, constructing a second-order RC equivalent circuit model; s3, parameters needing to be identified are determined; s4, parameter identification is carried out through a recursive least square method in combination with the battery experiment data at the corresponding environment temperature, and a parameter identification value at the corresponding environment temperature is obtained; and S5, a model parameter query table is established based on the parameter identification values at all the environment temperatures, so that the parameter identification values of the second-order RC equivalent circuit model at the corresponding environment temperatures can be obtained based on the input environment temperatures. According to the method, parameter identification is carried out on the second-order RC equivalent circuit model based on battery experiment data at different environment temperatures, and dynamic characteristics of the battery under different temperature conditions are accurately reflected, so that the precision and reliability of the model are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery modeling and parameter identification, and in particular to a method for identifying parameters of a lithium battery second-order RC equivalent circuit model based on ambient temperature. Background Art

[0002] With the rapid development of new energy technologies, lithium batteries, as an efficient and environmentally friendly energy storage device, have been widely used in electric vehicles, portable electronic devices, and renewable energy systems. In order to accurately evaluate the performance of lithium batteries, it is crucial to understand their internal dynamic behavior.

[0003] As a simplified electrochemical model, the second-order RC equivalent circuit model of lithium batteries plays an important role in battery status monitoring, health status estimation and remaining capacity prediction. The second-order RC equivalent circuit model of lithium batteries is a model based on circuit theory, which reflects the dynamic response characteristics of the battery by simulating the resistance, capacitance and other components inside the battery. The model usually consists of open circuit voltage (OCV), ohmic internal resistance (R0), and two RC pairs (each RC pair includes a resistor and a capacitor), which represent different polarization processes inside the battery. This model has a simple structure and relatively few parameters, which is convenient for real-time calculation in practical applications.

[0004] The applicant has found that ambient temperature is one of the key factors affecting the performance of lithium batteries. As the temperature rises or falls, the chemical reaction rate, ion diffusion rate and activity of the electrode material inside the battery will change, thereby affecting the battery's charge and discharge performance, internal resistance, capacity and cycle life. For example, at high temperatures, the internal resistance of the battery will decrease, but the battery's capacity and cycle life may decrease due to thermal runaway and increased side reactions; at low temperatures, the battery's activity decreases and the internal resistance increases, resulting in limited battery charge and discharge capabilities and reduced capacity. Therefore, in order to accurately describe the dynamic behavior of lithium batteries at different ambient temperatures, the effect of ambient temperature on model parameters must be considered. Traditional modeling methods often ignore this point, resulting in the model being unable to accurately reflect the actual performance of the battery when the temperature changes. This may cause the control strategy of the battery management system to fail in actual applications, and even cause safety issues.

[0005] In summary, the parameter identification of the second-order RC equivalent circuit model of lithium batteries at different ambient temperatures has important theoretical and practical significance. It not only helps to improve the accuracy and reliability of the battery management system, but also helps to extend the service life of the battery, reduce the maintenance cost of the battery system, and provide strong support for the widespread application of new energy vehicles and renewable energy systems. Therefore, how to design a parameter identification method based on ambient temperature is a technical problem that needs to be solved urgently. Summary of the invention

[0006] In view of the above-mentioned deficiencies in the prior art, the technical problem to be solved by the present invention is: how to provide a parameter identification method for a second-order RC equivalent circuit model of a lithium battery based on ambient temperature, perform parameter identification on the second-order RC equivalent circuit model based on battery experimental data under different ambient temperatures, accurately reflect the dynamic characteristics of the battery under different temperature conditions, thereby improving the accuracy and reliability of the second-order RC equivalent circuit model.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] The parameter identification method of the second-order RC equivalent circuit model of lithium battery based on ambient temperature includes:

[0009] S1: Perform pulse charge and discharge tests on the battery at different ambient temperatures to obtain corresponding battery experimental data;

[0010] S2: Construct a second-order RC equivalent circuit model for the battery;

[0011] S3: Determine the parameters to be identified in the second-order RC equivalent circuit model;

[0012] S4: Perform parameter identification on the second-order RC equivalent circuit model by combining the battery experimental data at the corresponding ambient temperature through the recursive least square method to obtain the parameter identification value at the corresponding ambient temperature;

[0013] S5: establishing a model parameter query table based on the parameter identification values ​​at various ambient temperatures, so that the parameter identification values ​​of the second-order RC equivalent circuit model at the corresponding ambient temperature can be obtained based on the input ambient temperature.

[0014] Preferably, in step S1, the battery test data includes the battery available capacity;

[0015] The available battery capacity can be obtained by the following steps:

[0016] S101: Setting the ambient temperature in the thermostat;

[0017] S102: Fully charge the battery and place it in a constant temperature box for several hours;

[0018] S103: performing constant current discharge on the battery at a preset discharge rate, and stopping discharge when the battery reaches a discharge cut-off voltage, to obtain the available capacity of the battery.

[0019] Preferably, in step S1, the battery test data includes current value and terminal voltage value;

[0020] The current value and terminal voltage value are obtained by the following steps:

[0021] S111: Setting the ambient temperature in the thermostat;

[0022] S112: Fully charge the battery and place it in a constant temperature box for several hours;

[0023] S113: Connect the battery to the battery testing system, and perform a discharge test on the battery at a discharge rate of 1C, so that the battery SOC decreases by 10%;

[0024] S114: leaving the battery for several minutes and measuring the current and terminal voltage of the battery;

[0025] S115: Repeat steps S112 to S114 to record the current value and terminal voltage value of the battery at the ambient temperature.

[0026] Preferably, in step S2, a battery model is built using a Simscape module in a Simulink environment, wherein the battery model includes various components of a second-order RC equivalent circuit, including the open circuit voltage of the battery, a first RC parallel circuit, a second RC parallel circuit and an ohmic resistor, wherein the two parallel RC circuits simulate the polarization effect inside the lithium battery, and the ohmic resistor simulates the battery voltage drop; a corresponding second-order RC equivalent circuit model is established based on the battery model.

[0027] Preferably, in step S2, the continuous state space of the second-order RC equivalent circuit model is represented by the following formula:

[0028]

[0029] Where: x(t) represents the battery SOC at time t; U p and U s They represent the polarization voltage of the two RC parallel circuits of the battery; η is the coulomb efficiency; C b Indicates battery capacity; I b Indicates the battery current; U ocv represents the open circuit voltage of the battery; R0 represents the ohmic resistance; R p and R s Represents polarization resistance; C p and C s represents polarization capacitance; ξ represents ambient temperature.

[0030] Preferably, in step S3, the parameters to be identified in the second-order RC equivalent circuit model include the open circuit voltage U ocv , ohmic resistance R0, polarization resistance R p and R s And the polarization capacitance C p and C s .

[0031] Preferably, in step S4, the continuous state space of the second-order RC equivalent circuit model and the battery experimental data at the corresponding ambient temperature are converted into a least squares form;

[0032] The specific conversion steps are as follows:

[0033] S401: Laplace transform the continuous state space of the second-order RC equivalent circuit model to obtain Formula 1 in the s domain;

[0034] Formula 1 is expressed as:

[0035]

[0036] S402: Simplify Formula 1 into Formula 2;

[0037] Formula 2 is expressed as:

[0038] α1ΔUs 2 +α2ΔUs+ΔU=α3I b s 2 +α4I b s+α5I b ;

[0039] in:

[0040]

[0041] α1=τ p τ s ,α2=τ p +τ s ,α3=τ p τ s R0,α4=τ p R s +τ s R p +R0(τ p +τ s ),α5=R p +R s +R0;

[0042] S403: Perform an inverse Laplace transformation on Formula 2 to obtain Formula 3;

[0043] Formula 3 is expressed as:

[0044]

[0045] S404: performing discretization processing of the formula 3 using the Euler method to obtain the formula 4;

[0046] Formula 4 is expressed as:

[0047]

[0048] in:

[0049]

[0050] Where: k represents the sampling time of the battery experimental data; ΔU(k), ΔU(k-1), ΔU(k-2) represent the terminal voltage values ​​of k, k-1, and k-2 at the corresponding ambient temperature respectively; I b (k),I b (k-1),I b (k-2) represents the current value at the kth, k-1th, and k-2th moments at the corresponding ambient temperature, respectively; T represents the sampling step length;

[0051] S405: Convert formula 4 into the following least squares formula;

[0052] The least squares formula is expressed as:

[0053] y(k)=φ(k)θ+e(k);

[0054] in:

[0055] y(k)=ΔU(k),u(k)=I b (k);

[0056] Data vector: φ(k)=[ΔU(k-1) ΔU(k-2) I b (k) I b (k-1) I b (k-2)];

[0057] Parameter vector:

[0058] Where: y(k) represents the system output; e(k) represents the system noise.

[0059] Preferably, in step S4, the processing steps of performing parameter identification using the recursive least squares method are as follows:

[0060] S411: Convert the least squares formula into a matrix form of Formula 5: Let k = 1, 2, ..., N + n; expand the system output y(k) and noise e(k) to N dimensions, and expand φ(k) to (2n+1)·N dimensions;

[0061] Formula 5 is expressed as:

[0062] Y=Φθ+e

[0063] in:

[0064]

[0065] Y=[y(n+1) y(n+2)…y(n+N)] T ;

[0066] e=[e(n+1) e(n+2) … e(n+N)] T ;

[0067] S412: According to the least squares criterion, the universal function J(θ) is taken for formula 5;

[0068] The formula is:

[0069]

[0070] Let the partial derivative of J(θ) with respect to θ be equal to 0, that is:

[0071]

[0072] When Φ T When Φ is non-singular, there exists satisfy but

[0073] S413: Identify using the following formula:

[0074]

[0075] in:

[0076] K(k+1)βP(k+1)Φ(k+1);

[0077]

[0078] Where: Represents the estimated value of the parameter at the previous moment; represents the observed value at the current moment; y(k+1) represents the actual observed value of the system; y(k+1) and The difference is the prediction error of the system. The prediction error is multiplied by the gain term K(k+1) to obtain the correction value of the prediction value at that moment, and finally added to the estimated value at the previous moment to obtain the optimal estimate at that moment. When starting identification, you need to give a qualified and P(0), generally It can be any value, P(0) takes αI, where α is the unit matrix of the same order as the system, and α is taken as large as possible to speed up the convergence.

[0079] S414: Repeat step S413 to iteratively calculate the parameter vector

[0080] Preferably, in step S414, the parameters in the parameter vector θ are Substitute into Formula 2 and Formula 4 to calculate the parameter identification value at the corresponding ambient temperature.

[0081] Preferably, in step S5, after obtaining the parameter identification values ​​at various ambient temperatures, a one-dimensional lookup table is established with the horizontal axis being the ambient temperature and the vertical axis being the parameter identification values.

[0082] Compared with the prior art, the parameter identification method of the lithium battery second-order RC equivalent circuit model based on ambient temperature in the present invention has the following beneficial effects:

[0083] The present invention performs parameter identification on a second-order RC equivalent circuit model based on battery experimental data at different ambient temperatures, and establishes a model parameter query table based on parameter identification values ​​at different ambient temperatures. First, by conducting experiments on batteries at different ambient temperatures and identifying model parameters, the dynamic characteristics of the battery under different temperature conditions can be more accurately reflected, thereby improving the accuracy and reliability of the second-order RC equivalent circuit model. Secondly, the established query table allows the model parameters to be quickly acquired according to the input ambient temperature, so that the model can adapt to the battery behavior under different temperature conditions, enhancing the adaptability and flexibility of the model. Finally, in the battery management system, using this model parameter query table based on ambient temperature, key information such as the remaining capacity and health status of the battery can be more accurately estimated, which helps to optimize the use and maintenance strategies of the battery; at the same time, accurate model parameters help to more accurately predict the performance changes of the battery at different temperatures, and timely discover potential safety hazards such as overheating and overcharging, which helps to improve the safety of the battery system and extend the service life of the battery.

[0084] The present invention performs parameter identification on a second-order RC equivalent circuit model through the recursive least squares method. First, the recursive least squares method can update the model parameters in real time when new data arrives, so that the model can quickly adapt to changes in the battery state, thereby improving the real-time performance of parameter identification. Secondly, compared with the traditional batch least squares method, the recursive least squares method does not need to store and process all historical data, but only needs to retain the parameter estimates and covariance matrix of the previous moment, thereby reducing the computational complexity and storage requirements. Finally, the recursive least squares method can still maintain good parameter identification performance in the presence of noise interference and measurement errors, and has strong robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0086] Figure 1 The figure is a logic block diagram of the parameter identification method of the second-order RC equivalent circuit model of lithium batteries based on ambient temperature.

[0087] Figure 2 This is the circuit diagram of the battery model. DETAILED DESCRIPTION

[0088] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but only represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0089] It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In the description of the present invention, it should be noted that the orientation or position relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invention product is usually placed when used, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance. In addition, the terms "horizontal", "vertical", etc. do not mean that the components are required to be absolutely horizontal or suspended, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted. In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0090] The following is a further detailed description through specific implementation methods:

[0091] Example:

[0092] This embodiment discloses a method for identifying parameters of a second-order RC equivalent circuit model of a lithium battery based on ambient temperature.

[0093] like Figure 1 As shown, the parameter identification method of the second-order RC equivalent circuit model of a lithium battery based on ambient temperature includes:

[0094] S1: Perform pulse charge and discharge tests on the battery at different ambient temperatures to obtain corresponding battery experimental data;

[0095] S2: Construct a second-order RC equivalent circuit model for the battery;

[0096] S3: Determine the parameters to be identified in the second-order RC equivalent circuit model;

[0097] S4: Parameter identification of the second-order RC equivalent circuit model is performed by combining the battery experimental data at the corresponding ambient temperature through the recursive least squares (RLS) method to obtain the parameter identification value at the corresponding ambient temperature;

[0098] S5: establishing a model parameter query table based on the parameter identification values ​​at various ambient temperatures, so that the parameter identification values ​​of the second-order RC equivalent circuit model at the corresponding ambient temperature can be obtained based on the input ambient temperature.

[0099] In this embodiment, after the parameter identification values ​​at various ambient temperatures are obtained, a one-dimensional lookup table is established with the ambient temperature as the horizontal axis and the parameter identification value as the vertical axis.

[0100] The present invention performs parameter identification on a second-order RC equivalent circuit model based on battery experimental data at different ambient temperatures, and establishes a model parameter query table based on parameter identification values ​​at different ambient temperatures. First, by conducting experiments on batteries at different ambient temperatures and identifying model parameters, the dynamic characteristics of the battery under different temperature conditions can be more accurately reflected, thereby improving the accuracy and reliability of the second-order RC equivalent circuit model. Secondly, the established query table allows the model parameters to be quickly acquired according to the input ambient temperature, so that the model can adapt to the battery behavior under different temperature conditions, enhancing the adaptability and flexibility of the model. Finally, in the battery management system, using this model parameter query table based on ambient temperature, key information such as the remaining capacity and health status of the battery can be more accurately estimated, which helps to optimize the use and maintenance strategies of the battery; at the same time, accurate model parameters help to more accurately predict the performance changes of the battery at different temperatures, and timely discover potential safety hazards such as overheating and overcharging, which helps to improve the safety of the battery system and extend the service life of the battery.

[0101] The present invention performs parameter identification on a second-order RC equivalent circuit model through the recursive least squares method. First, the recursive least squares method can update the model parameters in real time when new data arrives, so that the model can quickly adapt to changes in the battery state, thereby improving the real-time performance of parameter identification. Secondly, compared with the traditional batch least squares method, the recursive least squares method does not need to store and process all historical data, but only needs to retain the parameter estimates and covariance matrix of the previous moment, thereby reducing the computational complexity and storage requirements. Finally, the recursive least squares method can still maintain good parameter identification performance in the presence of noise interference and measurement errors, and has strong robustness.

[0102] In order to better introduce the technical solution of the present invention, this embodiment is described through the following parts.

[0103] 1. Battery Experimental Data

[0104] In this embodiment, the battery test data includes the battery available capacity;

[0105] The available battery capacity can be obtained by the following steps:

[0106] S101: Setting the ambient temperature in the thermostat (in this embodiment, 35° C., 25° C., 15° C., etc.);

[0107] S102: Fully charge the battery and place it in a constant temperature box for several hours (set to 6 hours in this embodiment);

[0108] S103: The battery is discharged at a constant current at a preset discharge rate (set to 0.5C in this embodiment), and the discharge is stopped when the battery reaches the discharge cut-off voltage to obtain the available capacity of the battery.

[0109] In this embodiment, the battery test data includes current value and terminal voltage value;

[0110] The current value and terminal voltage value are obtained by the following steps:

[0111] S111: Setting the ambient temperature in the thermostat (in this embodiment, 35° C., 25° C., 15° C., etc.);

[0112] S112: Fully charge the battery and place it in a constant temperature box for several hours (set to 6 hours in this embodiment);

[0113] S113: Connect the battery to the battery testing system, and perform a discharge test on the battery at a discharge rate of 1C, so that the battery SOC decreases by 10%;

[0114] S114: leaving the battery for several minutes (set to 54 minutes in this embodiment), measuring the current and terminal voltage of the battery; the terminal voltage is regarded as the open circuit voltage of the battery;

[0115] S115: Repeat steps S112 to S114 several times (set to 10 times in this embodiment), and record the current value and terminal voltage value of the battery at the ambient temperature.

[0116] 2. Second-order RC equivalent circuit model

[0117] In this embodiment, the battery model is built using the Simscape module in the Simulink environment. Figure 2 As shown, the battery model includes various components of the second-order RC equivalent circuit, including the open circuit voltage of the battery, the first RC parallel circuit, the second RC parallel circuit and an ohmic resistor, wherein the two parallel RC circuits simulate the polarization effect inside the lithium battery, and the ohmic resistor simulates the battery voltage drop; a corresponding second-order RC equivalent circuit model is established based on the battery model.

[0118] Specifically, the continuous state space of the second-order RC equivalent circuit model is expressed by the following formula:

[0119]

[0120] Where: x(t) represents the battery SOC at time t; U p and U s They represent the polarization voltage of the two RC parallel circuits of the battery; η is the coulomb efficiency; C b Indicates battery capacity; I b Indicates the battery current; U ocv represents the open circuit voltage of the battery; R0 represents the ohmic resistance; R p and R s Represents polarization resistance; C p and C s represents polarization capacitance; ξ represents ambient temperature.

[0121] 3. Parameters to be identified

[0122] In this embodiment, the parameters to be identified in the second-order RC equivalent circuit model include the open circuit voltage U ocv , ohmic resistance R0, polarization resistance R p and R s And the polarization capacitance C p and C s .

[0123] 4. Recursive Least Squares (RLS)

[0124] In this embodiment, the continuous state space of the second-order RC equivalent circuit model and the battery experimental data at the corresponding ambient temperature are converted into the least squares form;

[0125] The specific conversion steps are as follows:

[0126] S401: Laplace transform the continuous state space of the second-order RC equivalent circuit model to obtain Formula 1 in the s domain;

[0127] Formula 1 is expressed as:

[0128]

[0129] S402: Simplify Formula 1 into Formula 2;

[0130] Formula 2 is expressed as:

[0131] α1ΔUs 2 +α2ΔUs+ΔU=α3I b s 2 +α4I b s+α5I b ;

[0132] in:

[0133]

[0134] α1=τ p τ s ,α2=τ p +τ s ,α3=τ p τ s R0,α4=τ p R s +τ s R p +R0(τ p +τ s ),α5=R p +R s +R0;

[0135] S403: Perform an inverse Laplace transformation on Formula 2 to obtain Formula 3;

[0136] Formula 3 is expressed as:

[0137]

[0138] S404: performing discretization processing of the formula 3 using the Euler method to obtain the formula 4;

[0139] Formula 4 is expressed as:

[0140]

[0141] in:

[0142]

[0143] Where: k represents the sampling time of the battery experimental data; ΔU(k), ΔU(k-1), ΔU(k-2) represent the terminal voltage values ​​of k, k-1, and k-2 at the corresponding ambient temperature respectively; I b (k),I b (k-1),I b (k-2) represents the current value at the kth, k-1th, and k-2th moments at the corresponding ambient temperature, respectively; T represents the sampling step length;

[0144] S405: Convert formula 4 into the following least squares formula;

[0145] The least squares formula is expressed as:

[0146] y(k)=φ(k)θ+e(k);

[0147] in:

[0148] y(k)=ΔU(k),u(k)=I b (k);

[0149] Data vector: φ(k)=[ΔU(k-1) ΔU(k-2) I b (k) I b (k-1) I b (k-2)];

[0150] Parameter vector:

[0151] Where: y(k) represents the system output; e(k) represents the system noise.

[0152] Since the least squares method needs to repeat the entire calculation process each time the observation data is updated, the recursive least squares algorithm is used on this basis to correct the estimated value according to the new observation data to reduce the amount of calculation. Specifically, the processing steps for parameter identification using the recursive least squares method are as follows:

[0153] S411: Convert the least squares formula into a matrix form of Formula 5: Let k = 1, 2, ..., N + n; expand the system output y(k) and noise e(k) to N dimensions, and expand φ(k) to (2n+1)·N dimensions;

[0154] Formula 5 is expressed as:

[0155] Y=Φθ+e

[0156] in:

[0157]

[0158] Y=[y(n+1) y(n+2)…y(n+N)] T;

[0159] e=[e(n+1) e(n+2) … e(n+N)] T ;

[0160] S412: According to the least squares criterion, the universal function J(θ) is taken for formula 5;

[0161] The formula is:

[0162]

[0163] Let the partial derivative of J(θ) with respect to θ be equal to 0, that is:

[0164]

[0165] When Φ T When Φ is non-singular, there exists satisfy but

[0166] S413: Identify using the following formula:

[0167]

[0168] in:

[0169] K(k+1)=P(k+1)Φ(k+1);

[0170]

[0171] Where: Represents the estimated value of the parameter at the previous moment; represents the observed value at the current moment; y(k+1) represents the actual observed value of the system; y(k+1) and The difference is the prediction error of the system. The prediction error is multiplied by the gain term K(k+1) to obtain the correction value of the prediction value at that moment, and finally added to the estimated value at the previous moment to obtain the optimal estimate at that moment. When starting identification, you need to give a qualified and P(0), generally It can be any value, P(0) takes αI, where α is the unit matrix of the same order as the system, and α is taken as large as possible to speed up the convergence.

[0172] S414: Repeat step S413 to iteratively calculate the parameter vector The parameters in the parameter vector θ are Substitute into Formula 2 and Formula 4 to calculate the parameter identification value at the corresponding ambient temperature.

[0173] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A method for identifying parameters of a second-order RC equivalent circuit model of a lithium battery based on ambient temperature, characterized in that: include: S1: Perform pulse charge and discharge tests on the battery at different ambient temperatures to obtain corresponding battery experimental data; S2: Construct a second-order RC equivalent circuit model for the battery; S3: Determine the parameters to be identified in the second-order RC equivalent circuit model; S4: Perform parameter identification on the second-order RC equivalent circuit model by combining the battery experimental data at the corresponding ambient temperature through the recursive least square method to obtain the parameter identification value at the corresponding ambient temperature; S5: establishing a model parameter query table based on the parameter identification values ​​at various ambient temperatures, so that the parameter identification values ​​of the second-order RC equivalent circuit model at the corresponding ambient temperature can be obtained based on the input ambient temperature.

2. The method for identifying parameters of a second-order RC equivalent circuit model of a lithium battery based on ambient temperature according to claim 1, characterized in that: In step S1, the battery test data includes the available capacity of the battery; The available battery capacity can be obtained by the following steps: S101: Setting the ambient temperature in the thermostat; S102: Fully charge the battery and place it in a constant temperature box for several hours; S103: performing constant current discharge on the battery at a preset discharge rate, and stopping discharge when the battery reaches a discharge cut-off voltage, to obtain the available capacity of the battery.

3. The method for identifying parameters of a second-order RC equivalent circuit model of a lithium battery based on ambient temperature as claimed in claim 2, characterized in that: In step S1, the battery test data includes current value and terminal voltage value; The current value and terminal voltage value are obtained by the following steps: S111: Setting the ambient temperature in the thermostat; S112: Fully charge the battery and place it in a constant temperature box for several hours; S113: Connect the battery to the battery testing system, and perform a discharge test on the battery at a discharge rate of 1C, so that the battery SOC decreases by 10%; S114: leaving the battery for several minutes and measuring the current and terminal voltage of the battery; S115: Repeat steps S112 to S114 to record the current value and terminal voltage value of the battery at the ambient temperature.

4. The method for identifying parameters of a second-order RC equivalent circuit model of a lithium battery based on ambient temperature according to claim 1, characterized in that: In step S2, a battery model is built using the Simscape module in the Simulink environment; and a corresponding second-order RC equivalent circuit model is established based on the battery model.

5. The method for identifying parameters of a second-order RC equivalent circuit model of a lithium battery based on ambient temperature according to claim 1, characterized in that: In step S2, the continuous state space of the second-order RC equivalent circuit model is expressed by the following formula: Where: x(t) represents the battery SOC at time t; U p and U s They represent the polarization voltage of the two RC parallel circuits of the battery; η is the coulomb efficiency; C b Indicates battery capacity; I b Indicates the battery current; U ocv represents the open circuit voltage of the battery; R0 represents the ohmic resistance; R p and R s Represents polarization resistance; C p and C s represents polarization capacitance; ξ represents ambient temperature.

6. The method for identifying parameters of a second-order RC equivalent circuit model of a lithium battery based on ambient temperature according to claim 5, characterized in that: In step S3, the parameters to be identified in the second-order RC equivalent circuit model include the open circuit voltage U ocv , ohmic resistance R0, polarization resistance R p and R s And the polarization capacitance C p and C s .

7. The method for identifying parameters of a second-order RC equivalent circuit model of a lithium battery based on ambient temperature according to claim 6, characterized in that: In step S4, the continuous state space of the second-order RC equivalent circuit model and the battery experimental data at the corresponding ambient temperature are converted into a least squares form; The specific conversion steps are as follows: S401: Laplace transform the continuous state space of the second-order RC equivalent circuit model to obtain Formula 1 in the s domain; Formula 1 is expressed as: S402: Simplify Formula 1 into Formula 2; Formula 2 is expressed as: a1ΔUs 2 +α2ΔUs+ΔU=α3I b s 2 +α4I b s+α5I b ; in: α1=τ p t s ,α2=τ p +t s ,α3=τ p t s R0,α4=τ p R s +t s R p +R0(τ p +t s ),α5=R p +R s +R0; S403: Perform an inverse Laplace transformation on Formula 2 to obtain Formula 3; Formula 3 is expressed as: S404: performing discretization processing of the formula 3 using the Euler method to obtain the formula 4; Formula 4 is expressed as: in: Where: k represents the sampling time of the battery test data; ΔU(k), ΔU(k-1), ΔU(k-2) represent the terminal voltage values ​​of k, k-1, and k-2 at the corresponding ambient temperature respectively; I b (k),I b (k-1),I b (k-2) represents the current value at the kth, k-1th, and k-2th moments at the corresponding ambient temperature, respectively; T represents the sampling step length; S405: Convert formula 4 into the following least squares formula; The least squares formula is expressed as: y(k)=φ(k)θ+e(k); in: y(k)=ΔU(k),u(k)=I b (k); Data vector: φ(k)=[ΔU(k-1) ΔU(k-2) I b (k) I b (k-1) I b (k-2)]; Parameter vector: Where: y(k) represents the system output; e(k) represents the system noise.

8. The method for identifying parameters of a second-order RC equivalent circuit model of a lithium battery based on ambient temperature according to claim 7, characterized in that: In step S4, the processing steps of parameter identification using the recursive least squares method are as follows: S411: Convert the least squares formula into a matrix form of Formula 5: Let k = 1, 2, ..., N + n; expand the system output y(k) and noise e(k) to N dimensions, and expand φ(k) to (2n+1)·N dimensions; Formula 5 is expressed as: Y=Φθ+e in: Y=[y(n+1) y(n+2) … y(n+N)] T ; e=[e(n+1) e(n+2) … e(n+N)] T ; S412: According to the least squares criterion, the universal function J(θ) is taken for formula 5; The formula is: Let the partial derivative of J(θ) with respect to θ be equal to 0, that is: When Φ T When Φ is non-singular, there exists satisfy but S413: Identify using the following formula: in: K(k+1)=P(k+1)Φ(k+1); Where: Represents the estimated value of the parameter at the previous moment; represents the observed value at the current moment; y(k+1) represents the actual observed value of the system; y(k+1) and The difference is the prediction error of the system. The prediction error is multiplied by the gain term K(k+1) to obtain the correction value of the prediction value at that moment, and finally added to the estimated value at the previous moment to obtain the optimal estimate at that moment. When starting identification, you need to give a qualified and P(0), generally It can be any value, P(0) takes αI, where α is the unit matrix of the same order as the system, and α is taken as large as possible to speed up the convergence. S414: Repeat step S413 to iteratively calculate the parameter vector 9. The method for identifying parameters of a second-order RC equivalent circuit model of a lithium battery based on ambient temperature according to claim 8, characterized in that: In step S414, the parameters in the parameter vector θ are Substitute into Formula 2 and Formula 4 to calculate the parameter identification value at the corresponding ambient temperature.

10. The method for identifying parameters of a second-order RC equivalent circuit model of a lithium battery based on ambient temperature according to claim 1, characterized in that: In step S5, after the parameter identification values ​​at various ambient temperatures are obtained, a one-dimensional lookup table is established with the horizontal axis being the ambient temperature and the vertical axis being the parameter identification values.

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