Self-adaptive sliding-mode observer lithium battery SoC estimation method and terminal equipment

Abstract

The invention belongs to the technical field of lithium batteries, and relates to an adaptive sliding-mode observer lithium battery SoC estimation method and terminal equipment, and the method comprises the following steps: S1, building a lithium battery equivalent circuit model, and deducing a state equation of a battery; s2, based on the battery state equation, establishing a traditional linear sliding mode observer; s3, based on a traditional linear sliding-mode observer, introducing a nonlinear terminal sliding-mode surface and a continuous control rate, and establishing an adaptive sliding-mode observer; and S4, proving the stability of the adaptive sliding-mode observer by using the Lyapunov stability theorem, and then estimating the battery model state parameter SoC by using the adaptive sliding-mode observer, the terminal device being used for implementing the method. The method can better track a true value in lithium battery SoC estimation, effectively inhibits buffeting, has better estimation precision and robustness, and has important practical significance for guaranteeing safe and stable operation of a new energy automobile.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium batteries, and relates to a method for estimating the SoC of a lithium battery using an adaptive sliding mode observer and a terminal device, which is applicable to the battery management system of new energy vehicles. Background Technique

[0002] Due to its advantages such as high energy density and long cycle life, lithium batteries have become the first choice for power batteries of new energy vehicles. As a complex non-linear system, lithium batteries have complex and variable characteristics that are mutually coupled. The SoC (State of Charge) is a parameter that characterizes its remaining power. It is an important basis for operators to reasonably arrange trips and reduce overcharging and over-discharging. At the same time, it is also an ideal consistency index for balanced management. The accurate estimation of SoC has become a research hotspot for power batteries.

[0003] Common SoC estimation methods include the characteristic parameter method, the ampere-hour integration method, the model method, and the data-driven method. The characteristic parameter method obtains the corresponding relationship between the SoC of the battery in different states and the battery characteristic parameters offline through characteristic experiments, and then obtains the current SoC by measuring the characteristic parameters. Currently, commonly used characteristic parameters include the open circuit voltage (OCV), the remaining capacity, the impedance spectrum, etc. The ampere-hour (Ah) integration method belongs to an open-loop estimation method, and obtains the change of SoC through the integration of current over time. Its estimation error accumulates and becomes larger over time. The data-driven method trains the mapping relationship between the battery state parameters and the SoC through different machine learning algorithms, and estimates the SoC by online measuring the state parameters. The data-driven method requires a large amount of offline experimental data as the data support for machine learning, such as voltage, current, temperature, etc. The neural network model is a typical representative of this method. It has good state estimation ability for non-linear systems, strong generalization ability, but is prone to overfitting and requires a large amount of experimental data support. The model method completes the SoC estimation based on the filtering algorithm and the equivalent model of the power battery. Common model methods include Kalman filtering, H ∞ filtering, and sliding mode observers, etc. The sliding mode observer is based on the sliding mode control theory, and has the advantages of simple design, insensitivity to modeling parameter changes and disturbances, and no need for system online identification. It can better overcome the uncertainty of the battery model and external interference, and then accurately estimate its internal state.

[0004] However, in the case of a Conventional Sliding Mode Observer (CSMO), since a linear sliding mode surface is adopted, the error function can only asymptotically converge to the equilibrium point, so the SoC estimation error can never reach zero. Moreover, the use of a discontinuous control law in the conventional sliding mode observer will cause obvious chattering in the estimation results, affecting the accuracy of the SoC estimation of lithium batteries. Therefore, further improvement is needed. Summary of the Invention

[0005] The object of the present invention is to provide a method and a terminal device for estimating the SoC of a lithium battery using an adaptive sliding mode observer, so as to improve the accuracy of the SoC estimation of the conventional sliding mode observer and reduce the chattering of the estimation results. First, based on the state equation of the lithium battery in the DP equivalent circuit model, a conventional sliding mode observer is established. Secondly, in order to overcome the defect that the objective function of the linear sliding mode surface can only asymptotically tend to zero and the switching term in the sliding mode observer is likely to cause chattering in the estimation results, a non-linear terminal sliding mode surface is introduced. Finally, a sliding mode control law is designed based on the equivalent sliding mode control, and the Lyapunov stability is proved.

[0006] To achieve the above object of the invention, the present invention proposes a method for estimating the SoC of a lithium battery using an adaptive sliding mode observer, including the following steps:

[0007] S1. Establish an equivalent circuit model of the lithium battery and derive the battery state equation;

[0008] S2. Based on the battery state equation, establish a conventional linear sliding mode observer;

[0009] S3. Based on the conventional linear sliding mode observer, introduce a non-linear terminal sliding mode surface and a continuous control law to establish an adaptive sliding mode observer;

[0010] S4. Use the Lyapunov stability theorem to prove the stability of the adaptive sliding mode observer, and then use the adaptive sliding mode observer to estimate the state parameter SoC of the battery model.

[0011] Preferably, in step S1, the equivalent circuit model of the lithium battery is a double-polarized equivalent circuit model, including a voltage source Uoc, an ohmic internal resistance R, and two RC loops; where Uoc represents the open-circuit voltage of the lithium battery; R represents the ohmic internal resistance of the lithium battery; the two RC loops composed of Rp, Cp and Rs, Cs jointly simulate the polarization process of the lithium battery, where Rp represents the concentration difference polarization resistance of the lithium battery, Cp represents the concentration difference polarization capacitance of the lithium battery, Rs represents the electrochemical polarization internal resistance, and Cs represents the electrochemical polarization capacitance.

[0012] Specifically, taking the DP equivalent circuit model as an example, the model equation is established as follows:

[0013]

[0014] Among them, Z(0) represents the SoC at the initial moment, and C n is the maximum available capacity of the current battery. Although the open-circuit voltage has a non-linear relationship with the SoC, the relationship between the open-circuit voltage and the SoC can be linearly represented by a series of piecewise linear functions as:

[0015] U oc = g(Z) = kZ + d(2)

[0016] Among them, k and d represent undetermined coefficients in different SoC ranges. Differentiating Equation (2) and combining with Equation (1) gives:

[0017]

[0018] Since the change in current I within one sampling period can be ignored, differentiating the terminal voltage U of Equation (1) with respect to time gives:

[0019]

[0020] Among them

[0021] Substituting U in Equation (1) and U in Equation (4) d into u p and u s and Z gives:

[0022]

[0023] Among them

[0024] The Δf 1 , Δf 2 , Δf 3 , Δf 4 in Equation (4) and Equation (5) represent the uncertainties caused by modeling errors, process noise, and measurement noise, and are considered bounded disturbance terms in the battery system. The above is the establishment process of the state equation of the DP model. For the identification of model parameters, the identification process will not be elaborated here.

[0025] Preferably, step S2 includes the following steps:

[0026] S21. According to the lithium battery equivalent circuit model established in step S1, establish a state equation and define an error function, and then obtain the state equation of the error function;

[0027] S22. Introduce a switching function and select an appropriate switching gain to overcome system modeling errors and uncertain terms, and establish a traditional linear sliding mode observer.

[0028] Specifically, a traditional linear sliding mode observer is established based on the above equivalent circuit model:

[0029]

[0030] Where L 1 、L 2 、L 3 、L 4 are the switching gains of the sliding mode observer, all of which are constants greater than zero. Define the error function as follows:

[0031]

[0032] The following error equation can be obtained from equation (6):

[0033]

[0034] For the terminal voltage error e 1 , select V 1 = 0.5e 1 2 as the Lyapunov function. The derivative of the Lyapunov function is:

[0035]

[0036] Selection of the switching gain of the sliding mode observer:

[0037] L 1 > max(e 2 | + |Δf 1 |)(10)

[0038] Then there is:

[0039]

[0040] Therefore, e 1 will enter the sliding mode in a finite time. When in the sliding mode, the uncertainty terms will be overcome, and the error asymptotically approaches zero, and the sliding mode observer converges. After the error converges, it can be assumed that e 1 = 0. From the equivalent control law, e 2 = (sgn(e 1 )) eq . Similarly, select the switching gains L 2 、L 3 、L 4 as follows:

[0041]

[0042] When the switching gains L 2 、L 3 、L4 When the value satisfies the above formula, the error e of the sliding mode observer 2 、e 3 、e 4 will all converge to 0. And from the equivalent control law, it is known that:

[0043]

[0044] So far, the four sliding mode observers will overcome the modeling error and converge to the true value in turn, so as to estimate the state parameter SoC of the lithium battery model.

[0045] Preferably, step S3 includes the following steps:

[0046] Step S31: Introduce a non-linear function on the basis of the traditional linear sliding mode surface. Based on the non-linear terminal sliding mode surface, improve the disadvantage that the objective function of the traditional linear sliding mode surface can only asymptotically approach zero, so that after the system reaches the sliding mode, the tracking error can converge to zero in a finite time;

[0047] Step S32: Design the sliding mode control law based on the equivalent sliding mode control, and establish an adaptive sliding mode observer. The sliding mode control law is shown in the following formula.

[0048] Specifically, select the form of the terminal sliding mode as:

[0049]

[0050] where x ∈ R 1 is the state variable, β > 0, p and q are positive odd numbers, and q < p < 2q. The time from any initial state x(0) ≠ 0 to reach the equilibrium state x = 0 along the sliding mode is:

[0051]

[0052] The improved adaptive sliding mode observer is designed as:

[0053]

[0054] Subtract the model state equations (4) to (5) from equation (16) to obtain the state equation of the tracking error:

[0055]

[0056] First, for e 1 Select the following terminal sliding mode surface:

[0057]

[0058] where β 1 、p 1 、q 1The selection of is the same as that of β, p, and q in Equation (14).

[0059] Secondly, a sliding mode control law is designed based on equivalent sliding mode control, as shown in Equation (19).

[0060]

[0061] The sliding mode control law u 1 consists of two parts. The equivalent control term u e1 ensures that the system reaches the switching surface; the switching robust control term u s1 ensures that the system does not leave the switching surface S 1 = 0 when disturbances and unknown changes occur. In Equation (19), is the adaptive switching gain, which is updated according to the following adaptation rate:

[0062]

[0063] where θ 1 is a constant greater than zero.

[0064] Preferably, the adaptive sliding mode observer is adaptively adjusted based on the adaptive switching gain, overcomes the modeling error and converges to the true value in sequence, so as to estimate the state parameter SoC of the lithium battery model.

[0065] In step S4, after the above-mentioned sliding surface and control rate are designed, it is also necessary to prove the Lyapunov stability to ensure the stability of the sliding mode observer.

[0066] Select the Lyapunov function as:

[0067]

[0068] where λ 1 is the ideal sliding mode switching gain. Then, the derivative of V 1 is obtained as:

[0069]

[0070] There exists a finite non-negative λ 1 satisfying is a constant greater than zero, such that satisfies the second Lyapunov criterion. Therefore, S 1 will enter the switching surface S = 0 in a finite time, and then the terminal voltage error e 1 will converge to the equilibrium point along the switching surface at the specified time.

[0071] When S 1 enters the sliding mode, the uncertain term Δf 1will be cancelled out and the error will converge to zero. After the error converges, it can be assumed that, According to the equivalent control method:

[0072]

[0073] Similarly, for e 2 , e 3 , e 4 select the following sliding mode surface function:

[0074]

[0075] Referring to the design method of Equation (19), the equivalent control terms u e2 , u e3 , u e4 and the switching robust control terms u s2 , u s3 , u s4 are designed as follows:

[0076]

[0077] wherein The adaptive update of the switching gain is performed according to the following rules:

[0078]

[0079] It can be proved through the same steps as in Equations (21) and (22) that the sliding mode surfaces S 2 , S 3 , S 4 tend to be stable and the error will reach the equilibrium point in a finite time. Correspondingly, according to the equivalent control method:

[0080]

[0081] So far, the design of the adaptive sliding mode observer is completed, and the observer will be stable in turn, thereby estimating the state parameter SoC of the lithium battery model.

[0082] The present invention also proposes a terminal device, including:

[0083] One or more processors;

[0084] A computer-readable medium for storing one or more computer-readable instructions;

[0085] When the one or more computer-readable instructions are executed by the one or more processors, the one or more processors implement the above-mentioned lithium battery SoC estimation method based on the adaptive sliding mode observer.

[0086] Compared with the prior art, the present invention has the following beneficial effects:

[0087] In the SoC estimation of lithium batteries, the present invention can better track the true value, effectively suppress chattering, and has better estimation accuracy and robustness, which has important practical significance for ensuring the safe and stable operation of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the principle of the present invention and the technical solutions in the implementation, the technical solutions related to the present invention will be further introduced below using the attached drawings. The following attached drawings are only partial embodiments of the present invention. For those skilled in the art, other technical solutions can be obtained based on the following attached drawings without creative efforts.

[0089] Figure 1 is the flowchart of estimating SoC of the present invention using an adaptive sliding mode observer in the embodiment;

[0090] Figure 2 is the model input working condition diagram of the present invention in the embodiment;

[0091] Figure 3 is the flowchart of obtaining experimental data of the present invention in the embodiment;

[0092] Figure 4 is the DP equivalent circuit model diagram of the present invention in the embodiment;

[0093] Figure 5 is the SoC estimation result diagram under the DST working condition in the embodiment;

[0094] Figure 6 is the SoC estimation result diagram under the ECE working condition in the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0095] The present invention will be further described below in conjunction with the attached drawings and specific embodiments. The illustrative embodiments and descriptions of this invention are used to explain the present invention, but not to limit the present invention.

[0096] This embodiment proposes a method for estimating the SoC of a lithium battery using an adaptive sliding mode observer, including the following steps:

[0097] S1. Establish an equivalent circuit model of the lithium battery and derive the battery state equation;

[0098] In this embodiment, the equivalent circuit model of the lithium battery is a double-polarization equivalent circuit model, including a voltage source Uoc, an ohmic internal resistance R, and two RC loops; where Uoc represents the open-circuit voltage of the lithium battery; R represents the ohmic internal resistance of the lithium battery; the two RC loops composed of Rp, Cp and Rs, Cs jointly simulate the polarization process of the lithium battery, where Rp represents the concentration difference polarization resistance of the lithium battery, Cp represents the concentration difference polarization capacitance of the lithium battery, Rs represents the electrochemical polarization internal resistance, and Cs represents the electrochemical polarization capacitance.

[0099] Specifically, as Figure 4 shown, taking the DP equivalent circuit model as an example, the model equation is established as follows:

[0100]

[0101] where Z(0) represents the SoC at the initial moment, and C n is the maximum available capacity of the current battery. Although the open-circuit voltage has a non-linear relationship with the SoC, the relationship between the open-circuit voltage and the SoC can be linearly represented by a series of piecewise linear functions as:

[0102] U oc = g(Z) = kZ + d(2)

[0103] where k and d represent undetermined coefficients in different SoC ranges. Differentiating Equation (2) and combining it with Equation (1) gives:

[0104]

[0105] Since the change in current I within one sampling period can be ignored, differentiating the terminal voltage U of Equation (1) with respect to time gives:

[0106]

[0107] where

[0108] Substituting U in Equation (1) and U in Equation (4) d into u p 、u s and Z gives:

[0109]

[0110] where

[0111] Δf 1 ,Δf 2 ,Δf 3 ,Δf 4Denote the uncertainty caused by modeling error, process noise, and measurement noise as a bounded disturbance term in the battery system. The above is the establishment process of the state equation of the DP model. The identification process of the model parameters will not be elaborated here.

[0112] S2. Based on the battery state equation, establish a traditional linear sliding mode observer;

[0113] First, establish a traditional linear sliding mode observer based on the above equivalent circuit model:

[0114]

[0115] where L 1 , L 2 , L 3 , L 4 are the switching gains of the sliding mode observer, all being constants greater than zero. Define the error function as follows:

[0116]

[0117] From equation (6), the following error equation can be obtained:

[0118]

[0119] For the terminal voltage error e 1 , select V 1 = 0.5e 1 2 as the Lyapunov function. The derivative of the Lyapunov function is:

[0120]

[0121] Selection of the switching gain of the sliding mode observer:

[0122] L 1 > max(e 2 | + |Δf 1 |)(10)

[0123] Then there is:

[0124]

[0125] Therefore, e 1 will enter the sliding mode in finite time. When in the sliding mode, the uncertain terms will be overcome, and the error asymptotically approaches zero, and the sliding mode observer converges. After the error converges, it can be assumed that e 1 = 0. From the equivalent control law, e 2 = (sgn(e 1 )) eq . Similarly, select the switching gains L 2 , L3 and L 4 are as follows:

[0126]

[0127] When the switching gains L 2 and L 3 and L 4 satisfy the above formula, the errors e 2 and e 3 and e 4 of the sliding mode observer will all converge to 0. And from the equivalent control law, it is known that:

[0128]

[0129] So far, the four sliding mode observers will overcome the modeling errors and converge to the true values in turn, so as to estimate the state parameter SoC of the lithium battery model.

[0130] S3. Based on the traditional linear sliding mode observer, introduce a non-linear terminal sliding mode surface and a continuous control law to establish an adaptive sliding mode observer;

[0131] Specifically, introduce a non-linear function on the basis of the traditional linear sliding mode surface. Based on the non-linear terminal sliding mode surface, improve the disadvantage that the objective function of the traditional linear sliding mode surface can only asymptotically approach zero, so that after the system reaches the sliding mode, the tracking error can converge to zero in a finite time.

[0132] In this embodiment, the form of the terminal sliding mode is selected as:

[0133]

[0134] where x ∈ R 1 is the state variable, β > 0, p and q are positive odd numbers, and q < p < 2q.

[0135] The time from any initial state x(0) ≠ 0 to reach the equilibrium state x = 0 along the sliding mode is:

[0136]

[0137] The improved adaptive sliding mode observer is designed as:

[0138]

[0139] Subtract the model state equations (4) to (5) from equation (16) to obtain the state equation of the tracking error:

[0140]

[0141] First, for e 1 Select the following terminal sliding mode surface:

[0142]

[0143] where β 1 , p 1 , q 1 are selected in the same way as β, p, q in formula (14).

[0144] Secondly, a sliding mode control law is designed based on equivalent sliding mode control, as shown in formula (19).

[0145]

[0146] The sliding mode control law u 1 consists of two parts. The equivalent control term u e1 ensures that the system reaches the switching surface; the switching robust control term u s1 ensures that the system does not leave the switching surface S 1 = 0. In formula (19), is the adaptive switching gain, which is updated according to the following adaptation rate:

[0147]

[0148] where θ 1 is a constant greater than zero.

[0149] Preferably, the adaptive sliding mode observer is adaptively adjusted based on the adaptive switching gain, overcomes the modeling error and converges to the true value in sequence, so as to estimate the state parameter SoC of the lithium battery model.

[0150] S4. Use the Lyapunov stability theorem to prove the stability of the adaptive sliding mode observer, and then use the adaptive sliding mode observer to estimate the state parameter SoC of the battery model.

[0151] Following the above embodiments, after the above sliding mode surface and control law are designed, it is also necessary to prove the Lyapunov stability to ensure the stability of the sliding mode observer.

[0152] Select the Lyapunov function as:

[0153]

[0154] where λ 1 is the ideal sliding mode switching gain. Then, the derivative of V 1 is obtained as:

[0155]

[0156] There exists a finite non - negative λ 1 satisfying is a constant greater than zero such that satisfies the second Lyapunov criterion. Therefore, S 1 will enter the switching surface S = 0 in finite time, and then the terminal voltage error e 1 will converge to the equilibrium point along the switching surface at the specified time.

[0157] When S 1 enters the sliding mode, the uncertain term Δf 1 will be canceled out and the error will converge to zero. After the error converges, it can be assumed that According to the equivalent control method:

[0158]

[0159] Similarly, for e 2 , e 3 , e 4 the following sliding mode surface functions are selected:

[0160]

[0161] Referring to the design method of Equation (19), the equivalent control terms u e2 , u e3 , u e4 and the switching robust control terms u s2 , u s3 , u s4 are designed as follows:

[0162]

[0163] wherein The adaptive update of the switching gain is performed according to the following rules:

[0164]

[0165] It can be proved by the same steps as in Equations (21) and (22) that the sliding mode surfaces S 2 , S 3 , S 4 tend to be stable and the error will reach the equilibrium point in finite time. Accordingly, according to the equivalent control method:

[0166]

[0167] So far, the design of the adaptive sliding mode observer is completed, and the observer will be stable in turn, thereby estimating the state parameter SoC of the lithium battery model.

[0168] Following the above embodiments, the present invention also proposes a terminal device, including:

[0169] one or more processors;

[0170] a computer-readable medium for storing one or more computer-readable instructions;

[0171] When the one or more computer-readable instructions are executed by the one or more processors, the one or more processors implement the above-mentioned adaptive sliding mode observer lithium battery SoC estimation method.

[0172] The beneficial effects of the present invention are verified by the following specific implementation cases.

[0173] Implementation Cases:

[0174] Based on the independently built experimental platform, the voltage and current data corresponding to the actual operating conditions of the battery are obtained to simulate the actual working state of the battery, and provide a basis for verifying the advancement of the adaptive sliding mode observer lithium battery SoC estimation method proposed in the present invention (and referred to as the estimation algorithm of the present invention).

[0175] Refer to the typical American Dynamic Stress Test (DST) working conditions and the United Nations Economic Commission for Europe (ECE) automotive regulations working conditions, appropriately reduce the proportion, and design simulation working conditions. Figure 2 As shown in the figure, they are called DST condition and ECE condition respectively. In the condition, the current greater than zero indicates battery discharge, and less than zero indicates battery charging. The condition includes the working states of lithium battery charging, discharging, and shelving. The experimental data acquisition process is as follows Figure 3 shown.

[0176] First, the corresponding working conditions are set in the host computer equipped with Neware BTS 8.0. Then, the charging and discharging of the lithium battery is controlled by the Neware programmable electronic load. The sensor collects the voltage and current signals and transmits the data back to the host computer.

[0177] The verification was completed in Matlab 2023a / Simulink environment based on the measured working condition data. During the verification process, the four state variables U, U d ,u s , the initial value of Z is set to [0,0,0,0.9] to observe the robustness of the estimation algorithm of the present invention. Since there is no measurement error in the simulation process, the SoC obtained by ampere-hour integration is taken as the true value. The parameters of the adaptive sliding mode observer are designed as follows: β 1 =0.1,β 2 =6×10 -5 , β 3 =1×10 -5 , β4 = 1×10 -4 , q i = 3, p i = 5, θ 1 = 1×10 -10 , θ 2 = 1×10 -9 , θ 3 = 1×10 -9 , θ 4 = 1×10 -5 . In addition, a fixed step size of 0.5 seconds is used for the simulation, which is consistent with the sampling frequency of the experimental data. The specific estimation process of the estimation method of the present invention is as follows Figure 1 shown.

[0178] Under two working conditions, the estimation results of the adaptive sliding mode observer of the present invention are as follows Figure 5 , Figure 6 shown. The mean absolute error (MAE) and root mean square error (RMSE) of the estimation results under different working conditions are shown in Table 1.

[0179] From Figure 5 , Figure 6As can be seen from Table 1, the Adaptive Sliding Mode Observer (SGASMO) of the present invention can better track the true value and effectively suppress chattering compared with the traditional sliding mode observer and the improved sliding mode observers of the same type. For the Conventional Sliding Mode Observer (CSMO), since a linear sliding mode surface is adopted, the error function can only asymptotically converge to the equilibrium point, so the SoC estimation error can never reach zero. Moreover, the discontinuous control law adopted by the traditional sliding mode observer results in obvious chattering in the estimation results. In the Improved Sliding Mode Observer (ISMO), although the estimation algorithm adopts an adaptive sliding mode gain, which improves the estimation accuracy compared with the traditional sliding mode observer, there is still chattering caused by the high-frequency switching of the switching function. The Integral-Type Terminal Sliding Mode Observer (ITSMO) method reduces the chattering phenomenon in the estimation results by using the integral of the switching function as the control input; however, it sets the model parameters as constants and adopts a fixed switching gain, ignoring the characteristics that the model parameters change with the SoC and the upper bound of the disturbance may change with different operating condition currents, resulting in a decrease in the estimation accuracy. Generally speaking, the adaptive sliding mode observer of the present invention has less chattering and higher estimation accuracy compared with the improved sliding mode observers of the same type.

[0180] Table 1 Mean Absolute Error and Root Mean Square Error under Different Operating Conditions

[0181]

[0182] Based on the measured operating condition data, this embodiment verifies that the adaptive sliding mode observer proposed in the present invention has better estimation accuracy and robustness compared with the traditional sliding mode observer and other currently mainstream improved sliding mode observers by building a simulation model.

[0183] It should be noted that this application can be implemented in software and / or a combination of software and hardware. For example, it can be implemented using an Application Specific Integrated Circuit (ASIC), a general-purpose computer, or any other similar hardware device. In one embodiment, the software program of this application can be executed by a processor to implement the steps or functions described above. Similarly, the software program (including related data structures) of this application can be stored in a computer-readable recording medium, such as a RAM memory, a magnetic or optical drive, or a floppy disk and similar devices. In addition, some steps or functions of this application can be implemented using hardware, for example, as a circuit that cooperates with the processor to execute each step or function.

[0184] In addition, a part of the present application can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the present application through the operations of the computer. The program instructions for invoking the methods of the present application may be stored in a fixed or removable recording medium, and / or transmitted through a data stream in a broadcast or other signal-bearing medium, and / or stored in the working memory of a computer device running according to the program instructions. Herein, an embodiment according to the present application includes a device, which includes a memory for storing computer program instructions and a processor for executing the program instructions. When the computer program instructions are executed by the processor, the device is triggered to run based on the methods and / or technical solutions according to the foregoing multiple embodiments of the present application.

[0185] For those skilled in the art, it is obvious that the present application is not limited to the details of the above-mentioned exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the present application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present application is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed within the present application. Any reference signs in the claims should not be construed as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or devices stated in the apparatus claims can also be implemented by one unit or device through software or hardware. The words such as "first" and "second" are used to denote names and do not denote any particular order.

[0186] The technical solutions of the present invention are not limited to the limitations of the above specific embodiments. Any technical variations made according to the technical solutions of the present invention fall within the protection scope of the present invention.

Claims

1. An adaptive sliding mode observer lithium battery SoC estimation method, characterized in that: The following steps are involved: S1. Establish a lithium battery equivalent circuit model and derive the battery state equation; S2. Based on the battery state equation, a traditional linear sliding mode observer is established; S3, based on the traditional linear sliding mode observer, introduce the nonlinear terminal sliding mode surface and continuous control rate to establish an adaptive sliding mode observer; S4. Lyapunov stability theorem is used to prove the stability of the adaptive sliding mode observer, and then the adaptive sliding mode observer is used to estimate the battery model state parameter SoC.

2. The method for estimating SoC of a lithium battery using an adaptive sliding mode observer according to claim 1, characterized in that: In step S1, the lithium battery equivalent circuit model is a dual-polarization equivalent circuit model, including a voltage source U oc , an ohmic internal resistance R and two RC loops; among them, U oc represents the open circuit voltage of the lithium battery; R represents the ohmic internal resistance of the lithium battery; R p , C p With R s , C s The two RC loops formed jointly simulate the polarization process of lithium batteries, where R p Indicates the concentration difference polarization resistance of lithium battery, C p Represents the concentration difference polarization capacitance of lithium battery, R s represents the electrochemical polarization internal resistance, C s Represents electrochemical polarization capacitance.

3. The method for estimating SoC of a lithium battery using an adaptive sliding mode observer according to claim 2, characterized in that: Step S2 includes the following steps: S21, according to the lithium battery equivalent circuit model established in step S1, establish a state equation and define an error function, and then obtain a state equation of the error function; S22. Introduce the switching function and select the appropriate switching gain to overcome the system modeling error and uncertainty, and establish a traditional linear sliding mode observer.

4. The method for estimating SoC of a lithium battery using an adaptive sliding mode observer according to claim 3, characterized in that: Step S3 includes the following steps: Step S31, introducing a nonlinear function on the basis of the traditional linear sliding surface, based on the nonlinear terminal sliding surface, improving the shortcoming of the traditional linear sliding surface objective function that can only asymptotically approach zero, so that after the system reaches the sliding mode, the tracking error can converge to zero in a finite time; Step S32: Design a sliding mode control law based on equivalent sliding mode control and establish an adaptive sliding mode observer. The sliding mode control law is shown in the following formula: The sliding mode control law u1 consists of two parts, the equivalent control term u e1 ensures that the system reaches the switching surface, and the switching robust control term u s1 ensures that the system does not leave the switching surface S1 = 0 when disturbances and unknown changes occur, where β1>0, p1 and q1 are positive odd numbers, and q1 < p1 < 2q1; in the above formula is the adaptive switching gain, which is updated according to the following adaptation rate: In the above formula, θ1 is a constant greater than zero.

5. The method for estimating SoC of a lithium battery using an adaptive sliding mode observer according to claim 4, characterized in that: The adaptive sliding mode observer performs adaptive adjustment based on the adaptive switch gain, overcomes the modeling error and converges to the true value in sequence, thereby estimating the lithium battery model state parameter SoC.

6. A terminal device, characterized in that: include: one or more processors; a computer-readable medium for storing one or more computer-readable instructions; When the one or more computer-readable instructions are executed by the one or more processors, the one or more processors implement an adaptive sliding mode observer lithium battery SoC estimation method as described in any one of claims 1 to 5.

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