Lithium ion battery internal resistance and SOC joint estimation method based on fuzzy strategy guided PI observer

Abstract

The invention relates to a lithium ion battery internal resistance and SOC joint estimation method based on a fuzzy strategy guided PI observer, and belongs to the technical field of batteries, and the method comprises the following steps: S1, designing an appropriate extended PI observer, converting the internal resistance of a lithium ion battery into an augmented state, and carrying out the joint estimation of the internal resistance and the SOC; s2, developing an intelligent strategy based on information fusion and fuzzy logic guidance, and detecting the uncertainty of the model in real time by using a low-density outlier detection LOD concept and combining knowledge-based fuzzy logic based on the extended P observer designed in the step S1; and S3, according to a custom rule, changing control logic, adjusting internal polarization of the battery, realizing timely compensation of modeling defects, and ensuring accurate SOC estimation.

Description

Technical Field

[0001] The invention belongs to the technical field of batteries and relates to a method for jointly estimating the internal resistance and SOC of a lithium-ion battery based on a fuzzy strategy-guided PI observer. Background Art

[0002] The battery state of charge (SOC) is one of the most critical states in the BMS. Most management and control decisions need to be implemented based on the SOC value. Inaccurate battery SOC estimation can easily lead to battery overcharge or overdischarge, reduced battery balancing ability, reduced capacity, and even permanent damage. Therefore, accurate estimation of battery SOC is crucial to the safe, reliable and long-life operation of the battery system.

[0003] SOC estimation can be mainly divided into direct calculation method based on ampere-hour integration, method based on electrochemical model, method based on equivalent circuit model and data-driven method. In practical applications, the direct calculation method has uncertainty in the measurement of relevant parameters, resulting in limited accuracy; electrochemical models have defects such as model complexity, overfitting and high computational cost; data-driven requires a large amount of high-quality training data, and the model is prone to overfitting. Based on this, only the estimation method based on equivalent circuit model has high practical feasibility and great application potential due to its lower complexity, better accuracy and robustness. However, the equivalent circuit model is highly sensitive to battery parameters, so a joint estimation method is proposed. The current joint estimation method mainly combines recursive least squares RLS and Kalman filter and its variants for parameter identification and SOC collaborative estimation. The current joint estimation method still has certain limitations, for example, it assumes unrealistic Gaussian noise and has poor robustness to random scenarios. In addition, due to the low accuracy of the battery equivalent circuit model, it cannot show the true electrochemical response under high dynamic operating conditions, resulting in these model defects being misestimated as part of the internal resistance, thereby affecting the accuracy of battery SOC estimation. In existing studies, the uncertainties caused by different parameters are treated equally, without specific attention to the physical meaning of each battery parameter, and the specific impact of each parameter on output and accuracy is largely ignored. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a lithium-ion battery internal resistance and SOC joint estimation method based on fuzzy strategy guided PI observer. The internal resistance is converted into an augmented state by using an extended PI observer, and the defects of the model are detected and compensated in real time based on low density outlier detection (LOD) and fuzzy logic, effectively ensuring the adaptive control mechanism of model uncertainty perception, and realizing high-precision and high-robustness internal resistance and SOC estimation.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A method for jointly estimating internal resistance and SOC of a lithium-ion battery based on a fuzzy strategy-guided PI observer comprises the following steps:

[0007] S1: Design a suitable extended PI observer to transform the internal resistance of the lithium-ion battery into an augmented state and jointly estimate the internal resistance and SOC;

[0008] S2: Develop an intelligent strategy based on information fusion and fuzzy logic guidance, based on the extended P observer designed in step S1, using the concept of low-density outlier detection (LOD) combined with knowledge-based fuzzy logic to detect model uncertainty in real time;

[0009] S3: According to the custom rules, the control logic is changed to adjust the internal polarization of the battery to achieve timely compensation for modeling defects and ensure accurate SOC estimation.

[0010] Furthermore, the extended PI observer selected in step S1 is:

[0011]

[0012] Among them, K p and K i They are the proportional gain matrix and the integral gain matrix, S e K is the error signal fi Points.

[0013] Further, step S2 specifically includes the following steps:

[0014] S21: For each measured data value, calculate the relative outlier degree of the selected variable. When the value is close to or greater than 1, it indicates a large fluctuation;

[0015] S22: inputting the relative outlier value into the fuzzy logic system, and using the fuzzy logic system to classify the relative outlier value into three levels: low, medium, and high; in addition, the input describing the current state is also fed back to the fuzzy logic system;

[0016] S23: The fuzzy logic system gives decisions, which are divided into normal, normal compensation and model uncertainty scenarios.

[0017] Furthermore, in step S22, the fuzzy logic process is divided into three steps: fuzzification, rule formulation and defuzzification. The fuzzy logic model defines a number of rules, and the fuzzy rules are in the form of IF_THEN conditions.

[0018] Further, in step S2, the extended PI observer is described using the following expression:

[0019]

[0020] in,

[0021] x eo =[V1V2 soc R in ] T

[0022]

[0023] Among them, K peo , K ieo are the proportional and integral gain matrix functions respectively.

[0024] Further, in step S3, after confirming the model uncertainty, the control logic is changed according to the custom rules to adjust the internal polarization of the battery to mitigate the impact of the model defects until the system returns to a normal state and the accumulated error returns to its previous value. The specific idea of ​​correcting the modeling defects is:

[0025] Whenever the output of the model e k When fluctuations above the threshold occur, observe their scores and Icr k and dVoc k-1 After confirming the model uncertainty, the internal polarization of the battery is adjusted until the system returns to normal and the accumulated error returns to its previous value.

[0026] The beneficial effects of the present invention are:

[0027] (1) The proposed method takes into account modeling uncertainty and can reflect the true electrochemical response of the battery under highly dynamic conditions. The proposed method for jointly estimating the battery internal resistance and SOC has higher accuracy and robustness, and is computationally efficient, which has great potential in practical applications.

[0028] (2) The proposed method combines the intelligent strategy of information fusion and fuzzy logic for real-time detection of model defects with the extended PI observer, which can detect and compensate for model defects in real time, and can clearly explain the physical meaning of each battery parameter and clarify the specific impact of each parameter on output and accuracy.

[0029] 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

[0030] 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:

[0031] Figure 1This is a schematic diagram of the battery internal resistance and SOC combined estimation principle of the present invention;

[0032] Figure 2 It is the overall flow chart of the present invention;

[0033] Figure 3 It is a schematic diagram of the equivalent circuit model of the second-order battery of the present invention;

[0034] Figure 4 is a detailed flow chart of step S1 in an embodiment of the present invention;

[0035] Figure 5 is a detailed flow chart of step S12 in an embodiment of the present invention;

[0036] Figure 6 is a detailed flow chart of step S3 in an embodiment of the present invention;

[0037] Figure 7 are the parameters of the fuzzy logic model in step S3 in the example of the present invention. DETAILED DESCRIPTION

[0038] 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.

[0039] 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.

[0040] 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.

[0041] See also Figure 1 and Figure 2A lithium-ion battery internal resistance and SOC joint estimation method based on a fuzzy strategy guided PI observer includes the following steps:

[0042] S1: Parameter sensitivity analysis is performed using the second-order RC equivalent circuit model ECM and OCV-SOC mapping relationship to study the changes in the estimated performance of the battery model under different parameters, revealing that the battery internal resistance is the key parameter affecting the accuracy of lithium-ion state estimation.

[0043] like Figure 3 In step S1, the battery model is a second-order RC ECM, which has an open circuit voltage OCV source related to SOC. Using Kirchhoff's principle, the dynamics of the charge transfer region can be expressed as:

[0044]

[0045] Among them, I is the current, R1 and R2 are polarization internal resistances, C1 and C2 are polarization capacitances, and V1 represents the voltage drop caused by the charge transfer polarization internal resistance R1 and the double layer capacitance C1. Similarly, the kinetic equation of the diffusion process is:

[0046]

[0047] V2 represents the diffusion process simulated by the diffusion capacitor C2 and the concentration polarization internal resistance R2. For a given time interval t, the change in the battery state of charge is equal to the total amount of charge discharged or charged by the battery during the time interval. Therefore, the battery state of charge can be expressed as:

[0048]

[0049] Among them, η and C t Represent the Coulombic efficiency and the total capacity of the battery respectively. Figure 1 , the battery terminal voltage is:

[0050] V o =V oc (soc)+V1+V2+IR in

[0051] Among them, V o is the terminal voltage, R in Represents the internal resistance of the battery, V oc (soc) is an open circuit voltage source. Therefore, the battery ECM system can be expressed as a set of the following state equations:

[0052]

[0053] Convert the above expression into the following form:

[0054]

[0055] in,

[0056]

[0057] u=I,x=[V1V2 soc] T ,D=R in

[0058] h(x)=V oc (soc)+V1+V2

[0059] The internal state of the above LIBs system can be estimated using the following PI observer:

[0060]

[0061] Among them, K p and K i They are proportional gain matrix and integral gain matrix, S e K is the error signal fi Points.

[0062] See also Figure 4 , the steps for parameter sensitivity analysis using second-order RC ECM are:

[0063] S11: Before the experiment begins, all batteries are screened and capacity tested to find batteries with a capacity fluctuation of less than 3%;

[0064] S12: Clarify the OCV-SOC relationship mapping. Figure 5 The LIB cells were first charged at 0.25C using a constant current constant voltage (CCCV) method until the charge current was less than 0.01C; these fully charged cells were then placed under a discharge current of C / 30 until their V min (2.75V); After the battery has been left to stand for 2 hours, it is charged to V max (4.35V). Since the extremely low discharge current minimizes the polarization and hysteresis effects of the battery, the terminal voltage V o,ch It can be expressed as:

[0065] V o,ch =V oc (soc)+V in +I(R1+R2)

[0066] Similarly, the terminal voltage V o,dis It can be expressed as:

[0067] V o,dis =V oc (soc)-V in-I(R1+R2)

[0068] By taking the average of the charge and discharge curves, the OCV of the LIB battery can be obtained:

[0069] V oc (soc)=(V o,ch +V o,dis ) / 2

[0070] After obtaining the OCV of each point, the relationship between SOC and OCV within every two consecutive data points is as follows:

[0071] V oc (soc) = m i .soc+c i

[0072] in,

[0073]

[0074] After obtaining the OCV-SOV relationship, the battery was subjected to a series of hybrid pulse power characteristics (HPPC) tests. The main steps are as follows: (1) fully charged using the CCCV method at 0.25C until the charging current is less than 0.01C; (2) left standing for two hours; (3) discharged at 0.1C for 5% + left standing for 2 hours until V min ; (4) 0.1C charge 5% + stand for 2 hours until V max The measured battery data is processed by the particle swarm optimization algorithm PSO to identify the battery parameters.

[0075] S13: The measured battery data and parameter sensitivity are provided as time series input to the PI observer, and the adverse effects of battery parameter sensitivity are evaluated using indicators such as maximum absolute error (MaxAE), root mean square error (RMSE) and mean absolute error (MAE). The expression is:

[0076]

[0077] Among them, x oi and Represent the measured state value and the estimated state value respectively.

[0078] S2: For the uncertain key parameter of internal resistance, a suitable extended PI observer is selected to convert the internal resistance of the battery into an augmented state and jointly estimate the internal resistance and SOC; the selected PI observer is:

[0079]

[0080] Among them, K p and K iThey are the proportional gain matrix and the integral gain matrix, S e K is the error signal fi Points.

[0081] S3: Develop an intelligent strategy for real-time detection of model defects based on information fusion and fuzzy logic, and combine the extended PI observer, use the improved LOD concept, and combine knowledge-based fuzzy logic to detect model uncertainty in real time; then change the control logic according to the custom rules, adjust the internal polarization of the battery to achieve timely compensation of modeling defects, and ensure the estimation accuracy of states and parameters;

[0082] See also Figure 6 and Figure 7 ,The steps for effective detection of model uncertainty based on intelligent strategies of information fusion and fuzzy logic are:

[0083] S31: For each measured data value, calculate the relative outlier degree of the selected variable. When the value is close to or greater than 1, it indicates a large fluctuation. The calculation formula for the relative outlier degree is as follows:

[0084]

[0085] S32: Input the relative outlier values ​​into the fuzzy logic system, and then the system classifies them into three levels: low, medium, and high. In addition, the input describing the current state (i.e., whether the system has returned to normal and whether the accumulated error has returned to the previous value) will also be fed back to the fuzzy system. The fuzzy logic process can be divided into three steps: fuzzification, rule formulation, and defuzzification. The fuzzy logic system of the present invention defines a total of 27 rules. The fuzzy rules are in the form of IF_THEN conditions and have been verified by sufficient experiments.

[0086] S33: The fuzzy logic system gives decisions, which are divided into normal, normal compensation and model uncertainty scenarios;

[0087] S34: Correct the model defects. Whenever the output e based on the model k When fluctuations above the threshold occur, observe their scores and Icr k and dVoc k-1 After confirming the model uncertainty, the proposed method is used to adjust the internal polarization of the battery to mitigate the impact of model defects, thereby maintaining the accuracy of the system until the system returns to normal and the accumulated error returns to its previous value. The extended PI observer can be described using the following expression:

[0088]

[0089] in,

[0090] xeo =[V1 V2 soc R in ] T

[0091]

[0092]

[0093]

[0094] Among them, K peo , K ieo are the proportional and integral gain matrix functions respectively. In addition, the output of this design is is not a linear function of u.

[0095] S4: Verify the observability of the proposed method and experimentally verify the practicability of the proposed method under various random working conditions.

[0096] The observability verification method described in step S4 is: for a nonlinear system, the observability of the system is determined using the Li derivative theorem. According to the Li derivative, the battery system can be expressed as:

[0097]

[0098] If the gradient matrix of the Lie guide is full rank or has independent rows, then at any Observable.

[0099]

[0100] in,

[0101]

[0102] according to

[0103] x eo =[V l ,V2,socR in ] T

[0104] u=I,y=V o

[0105] A eo x eo =[-V l / R l C l V2 / R2C2 0 0] T

[0106] B eo =[1 / C l 1 / C2ηi / C t 0] T

[0107] h(x eo ,u)=V oc (soc)+V l +V2+R in u

[0108] The gradient of the Li derivative is calculated as:

[0109] dh eo =[1 1dV oc / dsoc I]

[0110]

[0111] Then the observability matrix is:

[0112]

[0113] When k V oc / dsoc k When ≠0 and I≠0, the above matrix is ​​full rank. Since OCV is a complex nonlinear function of the battery SOC, the first condition always holds. However, the unobservability when I=0 can be physically understood as, in this case, the enhanced state will be multiplied by zero and its effect will not even be reflected in the output voltage. Therefore, it is necessary to set [K p4eo ; K i4eo ] is zero to avoid misleading results.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 method for jointly estimating internal resistance and SOC of a lithium-ion battery based on a fuzzy strategy-guided PI observer, characterized in that: The following steps are involved: S1: Design a suitable extended PI observer to transform the internal resistance of the lithium-ion battery into an augmented state and jointly estimate the internal resistance and SOC; S2: Develop an intelligent strategy based on information fusion and fuzzy logic guidance, based on the extended P observer designed in step S1, using the concept of low-density outlier detection (LOD) combined with knowledge-based fuzzy logic to detect model uncertainty in real time; S3: According to the custom rules, the control logic is changed to adjust the internal polarization of the battery to achieve timely compensation for modeling defects and ensure accurate SOC estimation.

2. The method for jointly estimating internal resistance and SOC of a lithium-ion battery based on a fuzzy strategy guided PI observer according to claim 1, characterized in that: The extended PI observer designed in step S1 is: Among them, K p and K i They are proportional gain matrix and integral gain matrix, S e K is the error signal fi Points.

3. The method for jointly estimating internal resistance and SOC of a lithium-ion battery based on a fuzzy strategy guided PI observer according to claim 1, characterized in that: Step S2 specifically includes the following steps: S21: For each measured data value, calculate the relative outlier degree of the selected variable. When the value is close to or greater than 1, it indicates a large fluctuation; S22: inputting the relative outlier value into the fuzzy logic system, and using the fuzzy logic system to classify the relative outlier value into three levels: low, medium, and high; in addition, the input describing the current state is also fed back to the fuzzy logic system; S23: The fuzzy logic system gives decisions, which are divided into normal, normal compensation and model uncertainty scenarios.

4. The method for jointly estimating internal resistance and SOC of a lithium-ion battery based on a fuzzy strategy guided PI observer according to claim 3, characterized in that: In step S22, the fuzzy logic process is divided into three steps: fuzzification, rule formulation and defuzzification. The fuzzy logic model defines a number of rules, and the fuzzy rules are in the form of IF_THEN conditions.

5. The method for jointly estimating internal resistance and SOC of a lithium-ion battery based on a fuzzy strategy guided PI observer according to claim 4, characterized in that: In step S2, the extended PI observer is described using the following expression: in, x eo =[V1 V2 soc R in ] T Among them, K peo , K ieo are the proportional and integral gain matrix functions respectively.

6. The method for jointly estimating internal resistance and SOC of a lithium-ion battery based on a fuzzy strategy guided PI observer according to claim 1, characterized in that: In step S3, after confirming the model uncertainty, the control logic is changed according to the custom rules to adjust the internal polarization of the battery to mitigate the impact of the model defects until the system returns to a normal state and the accumulated error returns to its previous value. The specific idea of ​​correcting the modeling defects is: Whenever the output of the model e k When fluctuations above the threshold occur, observe their scores and Icr k and dVoc k-1 Find similar trends in the scores of After confirming the model uncertainty, the battery internal polarization is adjusted until the system recovers and the accumulated error returns to its previous value.

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