A method for constructing a PID framework for state estimation of large-size lithium-ion batteries

Abstract

The application relates to a PID framework construction method for large-size lithium ion battery state estimation, and belongs to the technical field of battery state estimation, and comprises the following steps: S1: modeling assumption and model correction processing are carried out on the large-size lithium ion battery, the battery is modeled, model deviation compensation is increased to compensate model uncertainty, and an equivalent circuit model ECM of the battery with nonlinearity, additional interference and measurement error is established; S2: observability analysis is carried out on the nonlinear battery system, and the observability of the battery ECM is judged; S3: a PID observer is designed based on the established battery model, an intelligent algorithm unit is established to monitor the internal state parameters of the battery, a differential control is introduced to act on the error change rate, and whether the proposed observer converges is analyzed; and S4: a characteristic test and a hybrid pulse power characteristic HPPC test are designed and carried out on the large-size lithium ion battery, the OCV-SOV relationship of the battery is determined, and the battery monomer parameters are acquired.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of battery state estimation, and relates to a PID framework construction method for large-size lithium ion battery state estimation. BACKGROUND

[0002] Lithium ion batteries, as a key medium in the energy storage field, have characteristics such as high energy density and long cycle life, and are an indispensable part of various electric vehicles and static energy storage systems. Since lithium ion batteries have complex nonlinear reaction kinetics and potential safety risks, it is necessary to introduce an intelligent battery management system (BMS) to ensure the safe operation of lithium ion batteries. The BMS uses measurable variables to estimate various battery states, and accurate state estimation can provide necessary state information for other battery management functions, such as battery balancing, discharge control, thermal management, health management, and safe operation. Among them, the state of charge (SOC) is the most critical state to ensure the effectiveness of the BMS.

[0003] Existing SOC estimation methods can be divided into direct calculation methods, ECM-based methods, electrochemical model-based methods, and data-driven methods. The direct calculation method calculates the SOC by calculating the number of charges flowing into / out of the battery cell and considering the initial value of the SOC, and its accuracy depends on the initial SOC and the measurement accuracy of the current sensor. Since current measurement will produce cumulative errors, there are great limitations in practical applications. The electrochemical model-based method involves the internal electrochemical reaction mechanism and has high simulation accuracy, but the electrochemical model involves mutually coupled partial differential equations and many parameters, and the solution is complex, and the parameter identification is difficult. The data-driven method usually does not involve any battery model, and the mapping relationship between the measurable feature input and the battery SOC is established to realize the SOC estimation of the battery. The quantity and quality of data are required to be high, and there are problems such as lack of high-quality training data, overfitting, and generalization. The equivalent circuit model-based method uses electrical elements (resistors, capacitors, etc.) to simulate the electrochemical dynamic response characteristics of lithium ion batteries, has low calculation complexity and easy-to-identify parameters, and is widely used in engineering.

[0004] The battery SOC estimation method based on ECM mainly includes filter-based and control observer-based methods. The filter-based method mainly uses Kalman filter series algorithms. Kalman filtering is a recursive process, in which each step redistributes the trust weight of the Ah-based and model-based SOC estimation, and the final value is corrected by the Kalman gain. These methods have good accuracy under certain assumptions; however, in practical applications, they are greatly affected by nonlinear conditions, external disturbances, and modeling errors, and recursive numerical iteration also increases the calculation complexity.

[0005] Control observer-based methods use a feedback control loop to eliminate the error between the estimated voltage and the measured value. These techniques are categorized as Luenberger observers, sliding mode observers, and proportional-integral observers. The Luenberger observer is simple to operate, but its accuracy degrades significantly in nonlinear situations. The sliding mode observer maintains good effectiveness in the presence of disturbances and uncertainties, but suffers from problems such as chattering in applications. The proportional-integral observer can achieve accurate SOC estimation by using nonlinear parameterization and adaptive mechanisms. However, because lithium-ion batteries have strong nonlinearities, control observer-based methods are difficult to adapt to actual conditions and lack accuracy in highly dynamic situations. In addition, the estimation accuracy and stability of observer-based methods are easily affected by factors such as modeling uncertainty and rapidly changing disturbances. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a PID framework construction method for state estimation of large-scale lithium-ion batteries. Based on the ECM of lithium-ion batteries, an augmented model deviation is proposed to compensate for the inaccuracy of the model in real time and improve the stability of SOC estimation. The observability of the system is discussed for nonlinear situations, and the effectiveness of the model is verified through experiments. Then, a new PID framework with an intelligent multi-layer error convergence mechanism is proposed based on the ECM, and the robustness and convergence of the proposed PID framework are verified by comparison with traditional observers.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A PID framework construction method for large-scale lithium-ion battery state estimation includes the following steps:

[0009] S1: Modeling assumptions and model corrections for large-size lithium-ion batteries, modeling for batteries, increasing model deviations to compensate for model uncertainty, and establishing a battery equivalent circuit model (ECM) with nonlinearity, additional interference, and measurement errors;

[0010] S2: Perform observability analysis on the nonlinear battery system to determine the observability of the battery ECM;

[0011] S3: Design a PID observer based on the established battery model, establish an intelligent algorithm unit to monitor the internal state parameters of the battery, introduce differential control to act on the error change rate, and analyze whether the proposed observer converges;

[0012] S4: Design and conduct characterization tests and hybrid pulse power characteristic HPPC tests on large-size lithium-ion batteries to determine the battery OCV-SOV relationship and obtain battery cell parameters.

[0013] Further, in step S1, a first-order RC equivalent circuit model is selected to describe the internal reaction process of the lithium ion battery for the NCM battery; and a first-order RC model with hysteresis is selected to describe the internal reaction process of the lithium ion battery for the LFP battery.

[0014] Further, in step S1, a first-order RC equivalent circuit model is selected, and the ECM expression is obtained from the Kirchhoff law as follows:

[0015]

[0016] wherein R1 is the ohmic internal resistance, R2 is the polarization internal resistance, C2 is the polarization capacitance, V oc (s) is the open-circuit voltage, and I is the input current.

[0017] In a given time t, the instantaneous value s(t) of the state of charge is equal to the initial state of charge s(0) of the battery plus the state of charge change Δs in this time period, and the difference in the state of charge of the battery in a time interval t is equal to the total charge stored or released from the battery monomer, so the instantaneous value expression of the battery SOC is:

[0018]

[0019] wherein η represents the coulombic efficiency of the battery monomer.

[0020] The battery output voltage is expressed by the following formula:

[0021] V o = V oc (s) + V1 + V2 = V oc (s) + V2 + IR1

[0022] Combining the above formula, the state equation of the battery ECM is:

[0023]

[0024] A model bias V b is added as an augmented state, and the corrected ECM is expressed as:

[0025]

[0026] The ECM with bias compensation is linearized and approximated, and the state equation is:

[0027]

[0028] The battery ECM with nonlinearity, additional disturbance and measurement error is established by the above formula:

[0029]

[0030] Wherein, f(u, x, t) represents a nonlinear function containing input, state or time, Q is a coefficient matrix representing the influence of different battery states on nonlinearity, w represents measurement error, and the state space equation of the battery ECM in the nonlinear scenario is obtained by combining the above formula:

[0031]

[0032] Further, in step S2, the observability of the system model is calculated using the Lie derivative tool theorem, and the system state equation is:

[0033]

[0034] Then the gradient matrix of the system Lie derivative is:

[0035] O=[df dL Ax 1 f dL B 1 f dL Ax 2 f dL B 2 f..] T

[0036]

[0037]

[0038]

[0039] L Ax k f(x)=L Ax (L Ax k-1 f(x));L b k f(x)=L b (L b k-1 f(x))

[0040] If O is a full rank matrix, the system is observable at any x0;

[0041] The established nonlinear lithium ion battery ECM state equation is rewritten as the following formula:

[0042]

[0043] Wherein, x=[V2 s V b ] T , u=I, y=V o ,B=[(1 / C2+Q1)(η i / Ct +Q2)Q3] T , Ax = [(-1 / R2C2)V20 0] T , f(x) = V oc (s) +V2+V b , D = R1;

[0044] The gradient of the Lie derivative is calculated to obtain the system observability matrix O1:

[0045]

[0046] The nonlinearity is taken as a state function, and the system state equation is rewritten, with x = [V2 s V b ] T , u = I, y = V o , Ax = [(-1 / R2C2+Q1)V2 Q2 Q3] T , D = R1, B = [(1 / C2)η i / C t 0] T , f(x) = V oc (s) +V2+V b ; the gradient of the Lie derivative is calculated to obtain the system observability matrix O2;

[0047] When the system uncertainty is measured, y = V o -w, the system state equation is rewritten to obtain x = [V2 s V b ] T , u = I, y = V o , Ax = [(-1 / R2C2)V2 Q2 Q3] T , B = [1 / C2 η i / C t 0] T , f(x) = V oc (s) +V2+V b , D = R1; the gradient of the Lie derivative is calculated to obtain the system observability matrix O3;

[0048] It is determined whether the matrices O1, O2, and O3 are full rank matrices to determine the observability of the proposed battery ECM; the battery OCV is a nonlinear function of SOC, and is always true, so the matrices O1, O2, and O3 are full rank matrices under any condition, that is, the system always maintains observability under any condition:

[0049]

[0050]

[0051] Further, the PID observer designed in step S3 satisfies the following conditions: only the derivative and integral components are activated in the battery cell SOC state when a significant error is detected; the differential control in the cell SOC is closed in the presence of model uncertainty or fluctuations, minimizing other control quantities; control of the augmented state including the differential part is strengthened; the differential control is completely closed in the presence of noise and rapidly changing disturbances; and is described as:

[0052]

[0053] where K p = [K p1 K p2 K p3 ] T ∈R 3×1 , K i = [K i1 K i2 K i3 ] T ∈R 3×1 and K d = [K d1 K d2 K d3 ] T ∈R 3 ×1 are the proportional, integral and differential gain matrices, respectively, g represents the integral of K fi multiplied by the error signal, and h represents the differential of the difference between the measured and true values of the battery cell output voltage;

[0054] The state error is expressed as The state vector of the error system is The error system is expressed as:

[0055]

[0056]

[0057] The matrix A e can be arbitrarily assigned when and only when the system is observable;

[0058] The gains of the observer are selected using the LQ method or the pole placement method to ensure that the error matrix A e remains a Hurwitz matrix, which indicates that the system is convergent, so that when t→∞, e→0 and g→0, the estimated state of the proposed PID observer will converge to the true state of the system.

[0059] Further, the battery experiments designed and implemented in step S4 are characteristic experiments and constant current discharge experiments, where the characteristic experiments are HPPC tests at different temperatures.

[0060] The present application has the advantages of:

[0061] (1) For large-size lithium-ion batteries, the present application proposes an augmented deviation concept based on lithium-ion battery ECM to compensate for modeling uncertainties in real time and improve model accuracy, and verifies the practicability of the model in various driving scenarios at different temperatures.

[0062] (2) The present application constructs a PID framework with intelligent multi-layer error convergence mechanism, which realizes higher accuracy, stronger robustness and faster convergence speed in the presence of high nonlinearity and rapid disturbance; the intelligent algorithm unit is introduced to obtain the internal state parameters of the battery, and the differential control is introduced to act on the error change rate, which can realize more stable and accurate state estimation of lithium-ion batteries.

[0063] Other advantages, objects and features of the present application will be set forth in part in the following specification, and in part will become apparent to those skilled in the art upon examination of the following specification, or can be learned from practice of the present application. The objects and other advantages of the present application can be realized and attained by the methods and instrumentalities described in the following specification. BRIEF DESCRIPTION OF DRAWINGS

[0064] In order to make the purposes, technical solutions and advantages of the present application clearer, the preferred detailed description of the present application will be made below in combination with the drawings, in which:

[0065] Figure 1 is the overall flowchart of the present application;

[0066] Figure 2 is the schematic diagram of the battery ECM in the present application;

[0067] Figure 3 is the flowchart for verifying the observability of the established battery ECM in the present application;

[0068] Figure 4 is the framework diagram of the PID control observer established in the present application;

[0069] Figure 5 is the experimental design diagram of the present application;

[0070] Figure 6 is the flowchart for verifying the robustness and convergence of the established PID framework in the present application. DETAILED DESCRIPTION

[0071] Following, the embodiments of the present application will be described in details by specific examples, and other advantages and effects of the present application can be easily understood by those skilled in the art from the disclosure of the present specification. The present application can also be implemented or applied by other different embodiments, and various modifications or changes can be made to the details in the present specification based on different views and applications without departing from the spirit of the present application. It should be noted that the drawings provided in the following examples only illustrate the basic concepts of the present application in a schematic manner, and the drawings only show the components related to the present application without drawing the number, shape and size of the components in actual implementation, the shapes, number and proportions of the components in actual implementation can be arbitrarily changed, and the layout pattern of the components can be more complex.

[0072] It should be noted that the drawings provided in the following examples only illustrate the basic concepts of the present application in a schematic manner, and the drawings only show the components related to the present application without drawing the number, shape and size of the components in actual implementation, the shapes, number and proportions of the components in actual implementation can be arbitrarily changed, and the layout pattern of the components can be more complex.

[0073] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application, however, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details, and in other embodiments, the known structures and devices are shown in the form of block diagrams rather than in the form of details to avoid making the embodiments of the present application difficult to understand.

[0074] Please refer to Figure 1 A PID framework construction method for state estimation of a large-size lithium ion battery includes the following steps:

[0075] S1: modeling assumption and model correction processing for a large-size lithium ion battery, modeling the battery, increasing model deviation compensation to compensate for model uncertainty, establishing an equivalent circuit model ECM of the battery with nonlinearity, additional disturbance and measurement error;

[0076] S2: observability analysis of the nonlinear battery system, to judge the observability of the ECM;

[0077] S3: design a PID observer based on the established battery model, establish an intelligent algorithm unit to monitor the internal state parameters of the battery, introduce a differential control to act on the error change rate, and analyze whether the proposed observer converges;

[0078] S4: select a large-size lithium ion battery, design and perform characteristic test and hybrid pulse power characteristic HPPC test, determine the OCV-SOV relationship of the battery and obtain the battery monomer parameters;

[0079] S5: Based on the established PID framework, design experiments and verify the model accuracy, and test this framework with Luenberger observer, etc. at the same time, compare the estimation accuracy of the proposed framework;

[0080] S6: Analyze the uncertainty of sensor measurement and disturbance, verify the effectiveness of intelligent differential control on additional noise and rapid disturbance, and design experiments to verify the convergence of the proposed framework.

[0081] Please refer to Figure 2 , in step S1, a first-order RC equivalent circuit model is selected for modeling, wherein R1 is the ohmic resistance, R2 is the polarization resistance, C2 is the polarization capacitance, V oc (s) is the open circuit voltage, I is the input current, and the ECM expression can be obtained from Kirchhoff's law:

[0082]

[0083] The SOC of the battery is the ratio of the available capacity to its nominal capacity. At a given time t, the instantaneous value s(t) of the state of charge is equal to the initial state of charge s(0) plus the state of charge change Δs during this period. Since the difference in the battery charge level within a time interval t is equal to the total charge stored or released from the battery monomer, the instantaneous value of the battery SOC is expressed as:

[0084]

[0085] where η represents the coulombic efficiency of the battery monomer. The battery output voltage can be expressed as:

[0086] V o = V oc (s) + V1 + V2 = V oc (s) + V2 + IR1

[0087] Combining the above formula, the state equation of the battery ECM is obtained as:

[0088]

[0089] Considering the model uncertainty of the actual working condition, a model deviation V b is added as an augmented state to compensate for model uncertainty and improve accuracy. The modified ECM is expressed as:

[0090]

[0091] Where the state part is linear, and the battery OCV and SOC have a complex nonlinear relationship, so the battery output part is nonlinear. The ECM with deviation compensation is linearized and approximated to obtain the state equation as:

[0092]

[0093] From the above formula, the following battery ECM with nonlinearity, additional interference and measurement error can be established:

[0094]

[0095] Where f(u, x, t) represents a nonlinear function containing input, state or time, Q is a coefficient matrix representing the influence of different battery states on nonlinearity, and w represents measurement error. Combining the above formula, the state space equation of the battery ECM in the nonlinear scenario is:

[0096]

[0097] Please refer to Figure 3 , the established battery ECM is a highly nonlinear system, and the observability of the system model can be calculated using the Lie derivative tool theorem. Let the system state equation be:

[0098]

[0099] Then the gradient matrix of the Lie derivative of the system is:

[0100] O=[df dL Ax 1 f dL B 1 f dL Ax 2 f dL B 2 f..] T

[0101]

[0102]

[0103]

[0104] L Ax k f(x)=L Ax (L Ax k-1 f(x));L b k f(x)=L b (L b k-1 f(x))

[0105] If O is a full rank matrix, the system is observable at any x0.

[0106] The established nonlinear lithium-ion battery ECM state equation is rewritten as follows:

[0107]

[0108] Where, x = [V2 s V b ] T , u=I,y=V o ,B=[(1 / C2+Q1)(η i / C t +Q2)Q3] T , Ax=[(-1 / R2C2)V20 0] T , f(x)=V oc (s)+V2+V b , D = R1. Calculate the gradient of the Lie derivative and obtain the system observability matrix O1 as:

[0109]

[0110] Taking nonlinearity as the state function, rewriting the system state equation, we have x=[V2 s V b ] T , u=I,y=V o ,Ax=[(-1 / R2C2+Q1)V2 Q2 Q3] T , D=R1,B=[(1 / C2)η i / C t 0] T ,f(x)=V oc (s)+V2+V b . Calculate the gradient of the Lie derivative and obtain the system observability matrix O2.

[0111] When measuring system uncertainty, y = V o -w, these uncertainties will be estimated as part of the enhanced bias. Rewriting the system state equation, we get x = [V2 s V b ] T , u=I,y=V o , Ax=[(-1 / R2C2)V2 Q2 Q3] T , B=[1 / C2η i / C t 0] T ,f(x)=V oc (s)+V2+V b , D = R1. Similarly, calculate the gradient of the Lie derivative to obtain the system observability matrix O3.

[0112] The rank of the matrix O1, O2, O3 is determined to determine the observability of the proposed battery ECM. The battery OCV is a nonlinear function of SOC, and is always true, then the matrix O1, O2, O3 is full rank in any case, that is, the system always maintains observability in any case.

[0113]

[0114]

[0115] Please refer to Figure 4 , in step S3, the battery state estimation method based on the control observer compares the battery voltage estimation value based on the ECM with the measured value, and uses feedback control to compensate for the error. For the high nonlinearity of the battery internal polarization, an intelligent algorithm unit is introduced to obtain the internal state parameters of the battery monomer, which separates the model defects and normal errors; differential control is introduced to act on the error change rate, which realizes faster convergence and more stable estimation in the case of nonlinearity and continuous change of error.

[0116] The effective application of the proposed PID controller requires the following conditions to be met: when a large error is detected, only the differential and integral components in the battery monomer SOC state are activated; in the presence of model uncertainty / fluctuations, close the differential control in the monomer SOC, minimize other control quantities. Strengthen the control of the augmented state including the differential part; in the presence of noise and rapidly changing disturbances, completely close the differential control.

[0117] The proposed framework can be described mathematically as:

[0118]

[0119] where K p = [K p1 K p2 K p3 ] T ∈R 3×1 , K i = [K i1 K i2 K i3 ] T ∈R 3×1 and K d = [K d1 K d2 K d3 ] T ∈R 3 ×1 are the proportional, integral and differential gain matrices, respectively, and g represents K fiThe integral of the error signal is multiplied by h, which represents the differential of the difference between the measured and true values of the battery cell output voltage.

[0120] The state error is denoted as The state vector of the error system is The error system can be represented as:

[0121]

[0122]

[0123] The matrix A is Hurwitz if and only if the system is observable. e The system remains observable in any case, so the gain of the observer can be chosen using the LQ method or the pole placement method to ensure that the error matrix A e remains a Hurwitz matrix, which indicates that the system is convergent. Therefore, when t→∞, e→0 and g→0, the estimated state of the proposed observer will converge to the true state of the system.

[0124] Please refer to Figure 5 , in the step S4, the designed and implemented battery experiments are battery characteristic experiments and constant current discharge experiments, wherein the characteristic experiments are HPPC tests at different temperatures.

[0125] Please refer to Figure 5 , in the step S5, the designed and implemented battery experiments are dynamic working condition experiments, including federal urban driving cycle FUDS, dynamic stress test DST, highway fuel economy test HFET and Beijing dynamic stress test BJ DST at different temperatures, to verify the estimation accuracy of the ECM considering the augmented model bias compared with the constant parameter ECM and the ECM based on RLS; and the proposed framework is compared with the Luenberger observer, the square root cubage Kalman filter SRCKF and the PI observer to evaluate the relative effectiveness of the proposed technology.

[0126] Please refer to Figure 6In step S6, the uncertainty of sensor measurement is represented by a non-zero mean bias signal, the battery cell is subjected to the same BJDST current signal and bias noise with standard deviation of 0.001 A and mean of 0.1 A, 0.5 A, 1 A, 1.5 A and 2 A; and subjected to the same voltage signal and time damping signal with standard deviation of 0.0001 V, 0.001 V, 0.01 V and 0.1 V, different statistical indicators are evaluated to verify the robustness of the proposed framework. The limited bandwidth white noise is added to verify the effectiveness of the intelligent differential control in the presence of additional noise and rapid disturbance. The convergence time of different observers is compared by designing experiments to verify the convergence of the proposed framework, experiments are respectively carried out with the initial SOC of the battery being 90% and 60%, data is transmitted to the host PC for state estimation under the FUDS current working condition, the convergence time of different observers is compared to evaluate the convergence effect of the proposed framework.

[0127] In the above embodiments, the specification refers to "this embodiment" to indicate that the specific features, structures or characteristics described in the embodiment are included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily all refer to the same embodiment.

[0128] In the above embodiments, although the present application has been described in conjunction with specific embodiments thereof, many alternatives, modifications and variations will be apparent to those skilled in the art in light of the foregoing description. For example, other storage structures (e.g., dynamic RAM (DRAM)) can use the embodiments discussed. Embodiments of the present application are intended to embrace all such alternatives, modifications and variations as can fall within the scope of the appended claims.

[0129] The embodiments also provide a computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements any of the methods in the embodiments.

[0130] The embodiments also provide an electronic terminal, comprising: a processor and a memory;

[0131] The memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory, so that the terminal executes any of the methods in the embodiments.

[0132] The computer readable storage medium in the embodiments can be understood by those skilled in the art that all or part of the steps of the above-mentioned method embodiments can be completed by a computer program related hardware. The foregoing computer program can be stored in a computer readable storage medium. The program is executed to perform the steps of the above-mentioned method embodiments; and the foregoing storage medium includes: ROM, RAM, magnetic disk or optical disk and various media that can store program codes.

[0133] The electronic terminal provided by the embodiment comprises a processor, a memory, a transceiver and a communication interface, the memory and the communication interface are connected with the processor and the transceiver and complete communication between each other, the memory is used for storing a computer program, the communication interface is used for communication, and the processor and the transceiver are used for running the computer program, so that the electronic terminal executes each step of the method.

[0134] In the embodiment, the memory can include a random access memory (RAM) and can also include a non-volatile memory, for example, at least one disk memory.

[0135] The processor described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP) and the like; and can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0136] The present application can be used in many general-purpose or special-purpose computing system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0137] The present application can be described in the general context of computer-executable instructions, such as program modules, executed by computers. Generally, program modules include routines, programs, objects, components, data structures, and the like, which perform particular tasks or implement particular abstract data types. The present application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in both local and remote computer storage media, including storage devices.

[0138] Finally, it is to be explained that the above embodiments are only used to illustrate the technical solutions of the present application but not to limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the purpose and scope of the technical solutions, and all should be covered in the scope of the claims of the present application.

Claims

1. A PID framework construction method for large-scale lithium-ion battery state estimation, characterized by: The following steps are involved: S1: Modeling assumptions and model corrections for large-size lithium-ion batteries, modeling for batteries, increasing model deviations to compensate for model uncertainty, and establishing a battery equivalent circuit model (ECM) with nonlinearity, additional interference, and measurement errors; A first-order RC equivalent circuit model is selected, in which the ohmic internal resistance is connected in series with the polarization internal resistance, and the polarization internal resistance is connected in parallel with the polarization capacitance. The ECM expression is obtained from Kirchhoff's law: in, is the ohmic internal resistance, is the polarization internal resistance, is the polarized capacitance, is the open circuit voltage, is the input current; At a given time Internal, instantaneous value of state of charge Equal to the battery's initial state of charge Plus the charge state change during this period , the battery charge level in a time interval The difference within is equal to the total charge stored or released from the battery cell, so the instantaneous value of the battery SOC is expressed as: in, Indicates the coulombic efficiency of the battery cell; The battery output voltage is expressed as follows: in It represents the voltage across the ohmic internal resistance. represents the voltage across the RC circuit; combined with the above formula, the state equation of the battery ECM is: Add a model bias As an augmented state, the modified ECM is expressed as: The ECM of deviation compensation is linearized and approximated to obtain the state equation: The battery ECM with nonlinearity, additional interference and measurement error is established by the above formula: in, represents a nonlinear function involving input, state, or time, is the coefficient matrix representing the nonlinear effects of different battery states, represents the measurement error. Combining the above equation, the state space equation of the battery ECM in the nonlinear scenario is: S2: Perform observability analysis on the nonlinear battery system to determine the observability of the battery ECM; S3: Design a PID observer based on the established battery model. Establish an intelligent algorithm unit to monitor the internal state parameters of the battery. Introduce differential control to act on the error change rate. Analyze whether the proposed observer converges. The designed PID observer meets the following conditions: When a significant error is detected, activate the differential and integral components only at the battery cell SOC state; in the presence of model uncertainty or fluctuations, disable the differential control in the cell SOC and minimize other control variables; strengthen the control of the augmented state, including the differential part; and completely disable the differential control under noise and rapidly changing interference. The description is: in, , and are the proportional, integral and differential gain matrices respectively, express Multiplied by the integral of the error signal, Indicates the differential of the difference between the measured value and the true value of the battery cell output voltage; The state error is expressed as , the state vector of the error system is , the error system is expressed as: If and only if the system is observable, the matrix Can be assigned any value; Use the LQ method or the pole location method to select the observer gains to ensure that the error matrix If remains a Hurwitz matrix, it means that the system is convergent. Therefore, when hour, and , the estimated state of the proposed PID observer will converge to the true state of the system; S4: Design and conduct characterization tests and hybrid pulse power characteristic HPPC tests on large-size lithium-ion batteries to determine the battery OCV-SOV relationship and obtain battery cell parameters.

2. The method for constructing a PID framework for state estimation of large-scale lithium-ion batteries according to claim 1, characterized in that: In step S1, for NCM batteries, a first-order RC equivalent circuit model is selected to describe the internal reaction process of lithium-ion batteries; for LFP batteries, a first-order RC model with hysteresis is selected to describe the internal reaction process of lithium-ion batteries.

3. The method for constructing a PID framework for state estimation of large-scale lithium-ion batteries according to claim 1, characterized in that: In step S2, the observability of the system model is calculated using the Lie derivative tool theorem, assuming that the system state equation is: Then the gradient matrix of the Lie derivative of this system is: if is a full rank matrix, then the system All are observable; The established nonlinear lithium-ion battery ECM state equation is rewritten as follows: in, , , , , , , ; Calculate the gradient of the Lie derivative to obtain the system observability matrix for: Taking nonlinearity as the state function, rewriting the system state equation, we have , , , , , , ; Calculate the gradient of the Lie derivative to obtain the system observability matrix ; When measuring system uncertainty , rewrite the system state equation to obtain , , , , , , ; Calculate the gradient of the Lie derivative to obtain the system observability matrix ; Judgment Matrix Is it a full rank matrix to judge the observability of the proposed battery ECM; the battery OCV is a nonlinear function of SOC, Always holds true, then in any case the matrix are all full-rank matrices, that is, the system always remains observable under any circumstances: 。 4. The method for constructing a PID framework for state estimation of large-scale lithium-ion batteries according to claim 1, characterized in that: The battery experiments designed and implemented in step S4 are battery characteristic experiments and constant current discharge tests, wherein the characteristic experiments are HPPC tests at different temperatures.

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