PID (Proportion Integration Differentiation) framework construction method for state estimation of large-size lithium ion battery

Abstract

The invention relates to a PID (Proportion Integration Differentiation) framework construction method for state estimation of a large-size lithium ion battery, which belongs to the technical field of battery state estimation, and comprises the following steps: S1, carrying out modeling assumption and model correction processing on the large-size lithium ion battery, carrying out modeling on the battery, increasing model deviation compensation model uncertainty, and carrying out modeling on the large-size lithium ion battery; establishing a battery equivalent circuit model ECM with nonlinearity, additional interference and measurement errors; s2, carrying out observability analysis on the nonlinear battery system, and judging the observability of the battery ECM; s3, designing a PID observer based on the established battery model, establishing an intelligent algorithm unit to monitor internal state parameters of the battery, introducing differential control to act on an error change rate, and analyzing whether the proposed observer is converged or not; and S4, designing the large-size lithium ion battery, carrying out a characteristic test and a mixed pulse power characteristic HPPC test, determining an OCV-SOV relationship of the battery, and obtaining battery monomer parameters.

Description

Technical Field

[0001] The invention 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 Art

[0002] As a key medium in the field of energy storage, lithium-ion batteries have the characteristics of high energy density and long cycle life. They are an indispensable part of various electric vehicles and static energy storage systems. Due to the complex nonlinear reaction dynamics and potential safety risks of lithium-ion batteries, it is necessary to introduce an intelligent battery management system BMS to ensure the safe operation of lithium-ion batteries. BMS uses measurable variables to estimate multiple battery states. 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 BMS.

[0003] Existing SOC estimation methods can be divided into direct calculation methods, ECM-based methods, electrochemical model-based methods, and data-driven methods. Direct calculation methods calculate SOC by calculating the number of charges flowing into / out of the battery cell and considering the initial value of SOC. Its accuracy depends on the initial SOC and the measurement accuracy of the current sensor. Because 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, which are complex to solve and difficult to identify parameters. The data-driven method usually does not involve any battery model. It realizes the SOC estimation of the battery by establishing a mapping relationship between measurable feature inputs and battery SOC. It has high requirements on the quantity and quality of data, and there are problems such as lack of high-quality training data, overfitting and generalization. The method based on the equivalent circuit model uses electrical components (resistors, capacitors, etc.) to simulate the electrochemical dynamic response characteristics of lithium-ion batteries. It has the characteristics of low computational complexity and easy parameter identification, and is widely used in engineering.

[0004] The battery SOC estimation methods based on ECM mainly include filter-based and control observer-based methods. The filter-based method mainly adopts the Kalman filter series algorithm. Kalman filtering is a recursive process in which each step redistributes the trust weights based on Ah 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 interference and modeling errors, and recursive numerical iterations also increase the computational complexity.

[0005] The control observer-based approach eliminates the error between the estimated voltage and the measured value through a feedback control loop. These techniques are divided into Luenberger observer, sliding mode observer and proportional integral observer. The Luenberger observer is simple to work, but its accuracy will be seriously reduced in nonlinear conditions; the sliding film observer still maintains good effectiveness in the presence of interference and uncertainty, but there are problems such as chattering in the application. The proportional integral observer can achieve accurate estimation of SOC by using nonlinear parameterization and adaptive mechanisms. However, because lithium-ion batteries have strong nonlinearity, the control observer-based approach is difficult to adapt well to actual conditions and lacks accuracy in high dynamic conditions. In addition, the estimation accuracy and stability of the observer-based approach 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 large-size lithium-ion battery state estimation, propose an augmented model deviation based on the ECM of the lithium-ion battery to compensate for the inaccuracy of the model in real time and improve the stability of the SOC estimation, discuss the observability of the system for nonlinear situations, and verify the effectiveness of the model 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 comparing with the traditional observer.

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

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

[0009] S1: Make modeling assumptions and model corrections for large-size lithium-ion batteries, model the battery, increase model deviation to compensate for model uncertainty, and establish 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 characteristic 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, 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.

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

[0015]

[0016] Among them, 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, I is the input current;

[0017] In a given time t, the instantaneous state of charge s(t) is equal to the initial state of charge s(0) of the battery plus the change in state of charge Δs during this time period. The difference in battery charge level within a time interval t is equal to the total charge stored or released from the battery cell. The instantaneous value of the battery SOC is expressed as:

[0018]

[0019] Wherein, η represents the coulombic efficiency of the battery cell;

[0020] The battery output voltage is expressed as follows:

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

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

[0023]

[0024] Add a model bias V b As an augmented state, the modified ECM is expressed as:

[0025]

[0026] The ECM of deviation compensation is linearized and approximated to obtain the state equation:

[0027]

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

[0029]

[0030] Among them, f(u,x,t) represents a nonlinear function including input, state or time, Q is a coefficient matrix representing the nonlinear influence of different battery states, and w represents the measurement error. Combining the above formula, the state space equation of the battery ECM in the nonlinear scenario is obtained as follows:

[0031]

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

[0033]

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

[0035] O=[df dL Ax 1 f B 1 f Ax 2 f 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, then the system is observable at any x0;

[0041] The established nonlinear lithium-ion battery ECM state equation is rewritten as follows:

[0042]

[0043] Where 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] Calculate the gradient of the Lie derivative and get the system observability matrix O1 as:

[0045]

[0046] 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 Lie derivative and obtain the system observability matrix O2;

[0047] When measuring system uncertainty, y = V o -w, rewrite the system state equation 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; calculate the gradient of Lie derivative and obtain the system observability matrix O3;

[0048] The observability of the proposed battery ECM is determined by judging whether the matrices O1, O2, and O3 are full-rank matrices; the battery OCV is a nonlinear function of SOC, and Always holds true, then in any case, the matrices O1, O2, and O3 are full-rank matrices, that is, the system always remains observable in any case:

[0049]

[0050]

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

[0052]

[0053] Among them, 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 proportional, integral and differential gain matrices respectively, g represents K fi Multiply by the integral of the error signal, h represents the differential of the difference between the measured value and the true value of the output voltage of the battery cell;

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

[0055]

[0056]

[0057] If and only if the system is observable, the matrix A e Can be assigned any value;

[0058] Use the LQ method or the pole location method to select the observer gains to ensure that the error matrix A e If it remains as a Hurwitz matrix, it means that the system is convergent. Therefore, 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] Furthermore, 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.

[0060] The beneficial effects of the present invention are:

[0061] (1) For large-size lithium-ion batteries, the present invention proposes the concept of augmented deviation based on the lithium-ion battery ECM, compensates for modeling uncertainty in real time and improves model accuracy, and verifies the practicality of the model in a variety of driving scenarios at different temperatures;

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

[0063] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0065] Figure 1 It is the overall flow chart of the present invention;

[0066] Figure 2 This is a schematic diagram of the battery ECM in the present invention;

[0067] Figure 3 A flow chart for verifying the observability of the established battery ECM in the present invention;

[0068] Figure 4 It is a schematic diagram of the framework of the PID control observer established in the present invention;

[0069] Figure 5 It is an illustration diagram of the experimental design of the present invention;

[0070] Figure 6 This is a flow chart for verifying the robustness and convergence of the established PID framework in the present invention. DETAILED DESCRIPTION

[0071] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0072] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

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

[0074] See also Figure 1 , a PID framework construction method for large-size lithium-ion battery state estimation includes the following steps:

[0075] S1: Modeling assumptions and model correction processing are carried out for a large-size lithium-ion battery. Modeling is carried out for the battery, model deviation is added to compensate for model uncertainty, and a battery equivalent circuit model ECM with nonlinearity, additional interference and measurement error is established;

[0076] S2: Perform observability analysis on the nonlinear battery system to determine 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 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 conduct characteristic tests and hybrid pulse power characteristic HPPC tests, determine the battery OCV-SOV relationship and obtain battery cell parameters;

[0079] S5: Based on the established PID framework, design experiments and verify the model accuracy. Test this framework simultaneously with the Luenberger observer and compare the estimation accuracy of the proposed framework.

[0080] S6: Analyze the uncertainty and disturbance of sensor measurements, verify the effectiveness of intelligent differential control for additive noise and fast disturbances, and design experiments to verify the convergence of the proposed framework.

[0081] See also Figure 2 In step S1, a first-order RC equivalent circuit model is selected for modeling, 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, I is the input current, and the ECM expression can be obtained from Kirchhoff's law:

[0082]

[0083] The SOC of a battery is the ratio of its available capacity to its nominal capacity. At a given time t, the instantaneous state of charge s(t) is equal to the battery's initial state of charge s(0) plus the change in state of charge Δs during that time period. Since the difference in battery charge levels over a time interval t is equal to the total charge stored or released from the battery cells, the instantaneous value of the battery SOC is expressed as:

[0084]

[0085] Where η represents the coulombic efficiency of the battery cell. The battery output voltage can be expressed as follows:

[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:

[0088]

[0089] Considering the model uncertainty of the actual working situation, a model deviation V is added b As an augmented state, compensating for model uncertainty and improving accuracy, the modified ECM is expressed as:

[0090]

[0091] The state part is linear, while 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:

[0092]

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

[0094]

[0095] Among them, f(u,x,t) represents a nonlinear function including input, state or time, Q is a coefficient matrix representing the nonlinear influence of different battery states, and w represents the measurement error. Combining the above formula, the state space equation of the battery ECM in the nonlinear scenario can be obtained as follows:

[0096]

[0097] See also Figure 3 The established battery ECM is a highly nonlinear system, and the observability of the system model can be calculated using the Li derivative tool theorem. Assume that the system state equation is:

[0098]

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

[0100] O=[df dL Ax 1 f B 1 f Ax 2 f 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, then 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 get 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, the gradient of the Lie derivative is calculated to obtain the system observability matrix O3.

[0112] The observability of the proposed battery ECM is determined by judging whether the matrices O1, O2, and O3 are full-rank matrices. The battery OCV is a nonlinear function of SOC, and If it always holds true, then in any case the matrices O1, O2, and O3 are full-rank matrices, that is, the system always remains observable in any case.

[0113]

[0114]

[0115] See also 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. In view of the high nonlinearity of the internal polarization of the battery, an intelligent algorithm unit is introduced to obtain the internal state parameters of the battery cell to separate the model defects from the normal errors; the differential control is introduced to act on the error change rate to achieve faster convergence and more stable estimation in the case of nonlinearity and continuous error changes.

[0116] The effective application of the proposed PID controller requires the following conditions to be met: activate the derivative and integral components only in the battery cell SOC state when a large error is detected; turn off the derivative control in the single cell SOC and minimize other control quantities in the presence of model uncertainty / fluctuation; strengthen the control of the augmented state including the derivative part; completely turn off the derivative control under noise and fast-changing disturbances.

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

[0118]

[0119] Among them, 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 proportional, integral and differential gain matrices respectively, g represents K fiMultiplied by the integral of the error signal, h represents the differential of the difference between the measured value and the true value of the battery cell output voltage.

[0120] The state error is expressed as The state vector of the error system is Then the error system can be expressed as:

[0121]

[0122]

[0123] If and only if the system is observable, the matrix A e Can be assigned any value. The system remains observable under any circumstances, so the gain of the observer can be selected using the LQ method or the pole location method to ensure that the error matrix A e If it remains a Hurwitz matrix, it means 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] See also Figure 5 In step S4, the battery experiments designed and implemented are battery characteristic experiments and constant current discharge tests, wherein the characteristic experiments are HPPC tests at different temperatures.

[0125] See also Figure 5 In step S5, the battery experiment designed and implemented is a dynamic operating condition experiment, including the Federal Urban Driving Cycle FUDS, the Dynamic Stress Test DST, the Highway Fuel Economy Test HFET and the Beijing Dynamic Stress Test BJDST at different temperatures, to verify the estimation accuracy of the ECM considering the augmented model deviation compared with the constant parameter ECM and the RLS-based ECM; and to compare the proposed framework with the Luenberger observer, the square root volume Kalman filter SRCKF and the PI observer to evaluate the relative effectiveness of the proposed technology.

[0126] See also Figure 6In step S6, a non-zero average bias signal is used to represent the uncertainty of sensor measurement. The battery cell uses the same BJDST current signal and adds bias noise with a standard deviation of 0.001A and average values ​​of 0.1A, 0.5A, 1A, 1.5A and 2A; and the same voltage signal is used and a time damping signal with a standard deviation of 0.0001V, 0.001V, 0.01V and 0.1V is added to evaluate different statistical indicators and verify the robustness of the proposed framework. White noise with limited bandwidth is added to verify the effectiveness of intelligent differential control in the presence of additional noise and rapid disturbances. Experiments are designed to compare the convergence time of different observers to verify the convergence of the proposed framework. Experiments are conducted with the initial battery SOC of 90% and 60%, respectively. Under the FUDS current condition, the data is transmitted to the host PC for state estimation, the convergence time of different observers is compared, and the convergence effect of the proposed framework is evaluated.

[0127] In the above embodiments, the description's reference to "this embodiment" indicates that a particular feature, structure, or characteristic described in conjunction with the embodiment is 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-described embodiments, although the invention has been described in conjunction with specific embodiments of the invention, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other storage structures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed. Embodiments of the invention are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of the appended claims.

[0129] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented.

[0130] This embodiment also provides an electronic terminal, including: a processor and a memory;

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

[0132] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0133] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0134] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

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

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

[0137] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may 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 may be located in local and remote computer storage media, including storage devices.

[0138] 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 it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A PID framework construction method for large-size lithium-ion battery state estimation, characterized by: The following steps are involved: S1: Make modeling assumptions and model corrections for large-size lithium-ion batteries, model the battery, increase model deviation to compensate for model uncertainty, and establish a battery equivalent circuit model ECM with nonlinearity, additional interference and measurement errors; 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, and analyze whether the proposed observer converges; S4: Design and conduct characteristic 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 PID framework construction method for large-size lithium-ion battery state estimation according to claim 1 is 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 PID framework construction method for large-size lithium-ion battery state estimation according to claim 1 is characterized in that: In step S1, a first-order RC equivalent circuit model is selected, and the ECM expression is obtained from Kirchhoff's law: Among them, 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, I is the input current; In a given time t, the instantaneous state of charge s(t) is equal to the initial state of charge s(0) of the battery plus the change in state of charge Δs during this time period. The difference in battery charge level within a time interval t is equal to the total charge stored or released from the battery cell. The instantaneous value of the battery SOC is expressed as: Wherein, η represents the coulombic efficiency of the battery cell; The battery output voltage is expressed as follows: V o =V oc (s)+V1+V2=V oc (s)+V2+IR1 Combined with the above formula, the state equation of the battery ECM is: Add a model bias V b As an augmented state, the modified ECM is expressed as: The ECM with 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: Among them, f(u,x,t) represents a nonlinear function including input, state or time, Q is a coefficient matrix representing the nonlinear influence of different battery states, and w represents the measurement error. Combining the above formula, the state space equation of the battery ECM in the nonlinear scenario is obtained as follows:

4. The PID framework construction method for large-size lithium-ion battery state estimation according to claim 1 is 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: O=[df dL Ax 1 f dL B 1 f dL Ax 2 f dL B 2 f..] T 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)) If O is a full rank matrix, then the system is observable at any x0; The established nonlinear lithium-ion battery ECM state equation is rewritten as follows: 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)V2 00] T , f(x) = V oc (s) + V2 + V b , D = R1;​ Calculate the gradient of the Lie derivative and get the system observability matrix O1 as: 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 Lie derivative and obtain the system observability matrix O2; When measuring system uncertainty, y = V o -w, rewrite the system state equation 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; calculate the gradient of Lie derivative and obtain the system observability matrix O3; The observability of the proposed battery ECM is determined by judging whether the matrices O1, O2, and O3 are full-rank matrices; the battery OCV is a nonlinear function of SOC, and Always holds true, then in any case, the matrices O1, O2, and O3 are full-rank matrices, that is, the system always remains observable in any case:

5. The PID framework construction method for large-size lithium-ion battery state estimation according to claim 1 is characterized in that: The PID observer designed in step S3 satisfies the following conditions: when a significant error is detected, the differential and integral components are activated only under the battery cell SOC state; In the presence of model uncertainty or fluctuations, turn off the differential control in the single SOC and minimize other control quantities; strengthen the control of the augmented state including the differential part; completely turn off the differential control under noise and rapidly changing interference; described as: Among them, 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 proportional, integral and differential gain matrices respectively, g represents K fi Multiply by the integral of the error signal, h represents the differential of the difference between the measured value and the true value of the output voltage of the battery cell; 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 A e 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 A e If it remains as a Hurwitz matrix, it means that the system is convergent. Therefore, when t→∞, e→0 and g→0, the estimated state of the proposed PID observer will converge to the true state of the system.

6. The PID framework construction method for large-size lithium-ion battery state estimation according to claim 1 is 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.

Images