CN116338513A - Lithium battery short-circuit fault detection method and system - Google Patents
Lithium battery short-circuit fault detection method and system Download PDFInfo
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Abstract
In order to detect the tiny change hidden in the environmental noise in the fault characteristics, the method and the system combine statistical information (accumulated sum) with the model-based observer fault estimation for the early diagnosis method of the battery short circuit, specifically combine the battery short circuit equivalent model with the battery fuzzy observer, and determine the battery short circuit fault according to the residual error of the battery state of charge actual value measured by the battery short circuit equivalent model and the battery state of charge estimated value estimated by the fuzzy observer.
Description
Technical Field
The application relates to the field of intelligent fault diagnosis of battery energy storage systems, in particular to a method and a system for detecting short-circuit faults of a lithium battery.
Background
Lithium ion batteries are widely applied to the field of energy storage, and soft short circuit of the lithium ion batteries can cause thermal runaway of the batteries, which is a potential safety threat. For the early diagnosis and detection of soft shorts. Various research methods are proposed, and diagnosis is performed by using a diagnosis method based on abnormal changes of the external characteristic parameters of the battery, namely, by using abnormal terminal voltage, temperature, SOC and other parameters of the battery; the soft short circuit detection method based on consistency difference is used for diagnosing by comparing the difference of the external characteristic parameters of the battery; the detection method is based on a model, and the internal short circuit detection problem is converted into a state estimation or parameter identification problem by means of a physical model of the battery; the internal short circuit detection method based on machine learning utilizes a machine learning algorithm to realize the detection of the internal short circuit and further analyze the relation between the internal short circuit and the thermal runaway and the internal short circuit and the short circuit triggering condition.
While most of the research efforts at present have recently contributed to soft short diagnosis and detection, these methods have limitations in technical or usage scenarios. The general fault detection method based on the analysis model needs to have strong universality (model universality, design universality and implementation universality).
Disclosure of Invention
Based on the problems, in order to diagnose the thermal runaway fault of the battery of the large-scale energy storage power station in time, a battery short circuit detection method based on a fuzzy observer and a battery short circuit equivalent model is provided.
In a first aspect, the present application provides a method for detecting a short-circuit fault of a lithium battery, including:
inputting the charge states of all the current battery at all times into a preset battery short-circuit equivalent model, and outputting battery state parameters of the battery at the next time by the battery short-circuit equivalent model;
estimating battery state estimation parameters of a current lithium battery by adopting a fuzzy observer;
and determining the short circuit fault of the battery according to the battery state parameter and the residual error of the battery state estimation parameter.
Preferably, the lithium battery short-circuit fault detection method further comprises:
modeling calculation is carried out on all charge states of a sample battery, and the battery short circuit equivalent model is obtained.
Preferably, the modeling calculation is performed for all states of charge of a sample battery to obtain the battery short-circuit equivalent model, including:
obtaining the charge state of the sample battery at the next moment according to the load current of the sample battery at the current moment and the initial charge state of the sample battery;
and establishing a battery short circuit equivalent model according to the state of charge of the sample battery at the current moment and the state of charge of the sample battery at the next moment.
Preferably, the obtaining the state of charge of the sample battery at the next moment according to the load current of the sample battery at the current moment and the initial state of charge of the sample battery includes:
integrating the load current of the sample battery at the current moment to obtain a current integral value;
and obtaining the state of charge of the sample battery at the next moment according to the initial state of charge and the current integral value.
Preferably, the method for detecting short-circuit fault of lithium battery further comprises:
and obtaining the fuzzy observer according to the battery voltage charge state curve and a preset robust observer.
Preferably, the obtaining the fuzzy observer according to the battery voltage charge state curve and a preset robust observer includes:
Obtaining an optimal weighting function of the battery state of charge according to the battery voltage state of charge curve and a preset Gaussian function;
and obtaining the fuzzy observer according to the optimal weighting function and the robust observer.
Preferably, the obtaining the optimal weighting function of the battery state of charge according to the battery voltage state of charge curve and a preset gaussian function includes:
obtaining an optimization coefficient of a Gaussian function according to the battery voltage charge state curve and a preset Gaussian function;
and according to the optimization coefficient, determining the optimal parameter of the Gaussian function, and further determining the optimal weighting function of the battery state of charge.
In a second aspect, the present application provides a lithium battery short-circuit fault detection system, including:
parameter calculation module: inputting the charge states of all the current battery at all times into a preset battery short-circuit equivalent model, and outputting battery state parameters of the battery at the next time by the battery short-circuit equivalent model;
parameter estimation module: estimating battery state estimation parameters of a current lithium battery by adopting a fuzzy observer;
and a fault detection module: and determining the short circuit fault of the battery according to the battery state parameter and the residual error of the battery state estimation parameter.
The invention also provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the method.
Meanwhile, the invention also provides a computer readable storage medium which stores a computer program for executing the method.
According to the technical scheme, in order to detect the tiny change hidden in the environmental noise in the fault characteristics, the statistical information (accumulated sum) is combined with the model-based observer fault estimation to be used for the early diagnosis method of the battery short circuit, specifically, the battery short circuit equivalent model and the battery fuzzy observer are combined to be used, and the battery short circuit fault is determined according to the residual error of the battery state of charge actual value measured by the battery short circuit equivalent model and the battery state of charge estimated value estimated by the fuzzy observer.
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of preferred embodiments, as illustrated in the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for detecting a short-circuit fault of a lithium battery in an embodiment of the application.
FIG. 2 is a flow chart of a weighted function self-tuning state and fault estimator method for battery short detection in an embodiment of the present application.
Fig. 3 is a schematic diagram of a battery short-circuit equivalent circuit model in an embodiment of the present application.
Fig. 4 is a schematic diagram of open circuit voltage-SOC curves of a lithium ion battery in an embodiment of the present application.
Fig. 5 is a schematic diagram of a design flow of a fuzzy observer based on a TS fuzzy system in an embodiment of the present application.
FIG. 6 is a schematic diagram of a blurring flow of an OCV-SOC curve in an embodiment of the present application.
FIG. 7 is a schematic diagram of self-adjusting fault estimation of a weighting function in an embodiment of the present application.
Fig. 8 is a schematic structural diagram of a short-circuit fault detection system of a lithium battery in an embodiment of the application.
Fig. 9 is a schematic structural diagram of an electronic device in an application embodiment.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Since most of the research efforts at present have recently contributed to soft short diagnosis and detection, these methods have limitations in terms of technology or use scenarios. The utility model-based general fault detection method needs to have stronger universality (model universality, design universality and implementation universality), and the application provides a weighting function self-regulating state and fuzzy observer for battery short circuit detection. In view of the slowly varying nature of battery SOC, based on genetic algorithms, a systematic approach has been proposed to construct self-regulating mechanisms to cope with nonlinear Open Circuit Voltage (OCV) -SOC curves. To detect small changes in fault signatures that are hidden in ambient noise, statistical information (cumulative sums) are combined with model-based observer fault estimates.
Based on the foregoing, the present application further provides a lithium battery short-circuit fault detection device for implementing the lithium battery short-circuit fault detection method provided in one or more embodiments of the present application, where the lithium battery short-circuit fault detection device may be in communication connection with a user client device, and the user client device may be provided with a plurality of lithium battery short-circuit fault detection devices, and specifically may access the client terminal device through an application server.
The lithium battery short-circuit fault detection device can receive a lithium battery short-circuit fault detection instruction from the client terminal device, obtain battery parameters such as a battery charge state and the like from the lithium battery short-circuit fault detection instruction, obtain a battery parameter actual value of the battery through a battery short-circuit equivalent model, obtain a battery parameter estimated value of the battery through a fuzzy observer, judge whether the battery is in a fault state through the actual value of the battery parameter and residual errors of the estimated value, and if the residual errors are 0, the battery normally operates, and if the residual errors are 1, the battery is in a short-circuit fault, and then the lithium battery short-circuit fault detection device can send the battery fault state to the client terminal device for display.
It is understood that the client devices may include smartphones, tablet electronic devices, portable computers, desktop computers, personal Digital Assistants (PDAs), and the like.
In another practical application, the part for detecting the short-circuit fault of the lithium battery can be performed in the classification processing center as described above, or all the operations can be completed in the client device. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The present application is not limited in this regard. If all operations are completed in the client device, the client device may further include a processor for performing specific processing of short-circuit fault detection of the lithium battery.
The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. For example, the communication unit may send the lithium battery short-circuit fault detection instruction to the server of the classification processing center, so that the server performs the lithium battery short-circuit fault detection processing according to the lithium battery short-circuit fault detection instruction. The communication unit can also receive the configuration result of the unit parameters returned by the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.
Any suitable network protocol may be used for communication between the server and the client device, including those not yet developed at the filing date of this application. The network protocols may include, for example, TCP/IP protocol, UDP/IP protocol, HTTP protocol, HTTPS protocol, etc. Of course, the network protocol may also include, for example, RPC protocol (Remote Procedure Call Protocol ), REST protocol (Representational State Transfer, representational state transfer protocol), etc. used above the above-described protocol.
In order to detect small changes hidden in environmental noise in fault characteristics, statistical information (accumulated sum) is combined with model-based observer fault estimation to be used for a battery short-circuit early diagnosis method, specifically, a battery short-circuit equivalent model and a battery fuzzy observer are combined to be used, and battery short-circuit faults are determined according to residual errors of battery state-of-charge actual values measured by the battery short-circuit equivalent model and battery state-of-charge estimated values estimated by the fuzzy observer.
The following embodiments and application examples are described in detail.
In order to diagnose a thermal runaway fault of a battery of a large-scale energy storage power station in time, the application provides an embodiment of a method for detecting a short-circuit fault of a lithium battery, referring to fig. 1, the method specifically comprises the following steps:
step 100: inputting the charge states of all the current battery at all times into a preset battery short-circuit equivalent model, and outputting battery state parameters of the battery at the next time by the battery short-circuit equivalent model;
step 200: estimating battery state estimation parameters of a current lithium battery by adopting a fuzzy observer;
step 300: and determining the short circuit fault of the battery according to the battery state parameter and the residual error of the battery state estimation parameter.
In the present embodiment, considering the slow variation characteristic of the battery SOC, a systematic method is proposed based on a genetic algorithm to construct a self-regulating mechanism to cope with a nonlinear Open Circuit Voltage (OCV) -SOC curve. In order to detect the tiny change hidden in the environmental noise in the fault characteristics, the statistical information (accumulated sum) is combined with the fault estimation of the observer based on the model, the whole strategy architecture is shown in fig. 2, a battery short circuit equivalent model is established first, and then the OCV-SOC curve and the robust observer are subjected to fuzzification to obtain the fuzzy observer.
The fuzzy observer is essentially a weighted function self-adjusting robust observer, and the battery parameter estimation value of the battery is obtained through the fuzzy observer, and the battery short circuit equivalent model is shown in figure 3. Resistor R 0 Represents an ohmic resistance that includes the resistance of the contacts, electrodes, and electrolyte. The double RC loop characterizes the charge transfer effect, diffusion effect and double-layer behavior inside the lithium ion battery, and can simulate the transient response of the battery. Furthermore, the dual RC network is a good tradeoff between model error and model complexity compared to single RC and triple RC structures. And obtaining the actual value of the battery parameter of the battery through the battery short circuit equivalent model, judging whether the battery is in a fault state or not through the residual error of the actual value and the estimated value of the battery parameter, wherein the battery normally operates if the residual error is 0, and the battery is in a short circuit fault if the residual error is 1.
Due to I in ＝I batt +I sc The battery equivalent circuit model with the short-circuit resistance in the i-th SOC interval is expressed as: x (k+1) =ax (k) +b f f(k)+B d d(k)
y(k)＝Cx(k)+Du(k)+D f f(k)+D d d(k) (7)
Wherein x (k) ∈R n Is a state vector; y (k) ∈R p Is the output; u (k) ∈R m Is a known input corresponding to I batt ；
Is a battery failure corresponding to I sc ；/>
Is of the type I 2 [0，∞]Is a disturbance of (2); b (B) d And D d Is a constant real matrix of appropriate dimensions.
The fault estimator is designed as a proportional-integral observer. The integral term not only can ensure the robust state estimation when the fault occurs, but also can simultaneously provide the fault estimation, and the fuzzy observer can be expressed as:
Wherein, the liquid crystal display device comprises a liquid crystal display device,
is an estimated state vector; />
Is the observer output; />
Is an estimated f (k); l epsilon R n×p And; />
Is the observer gain.
The error dynamics between model expression (7) and observer expression (8) can be described as equation (9):
wherein:
and is also provided with
Δf (k) =f (k+1) -f (k) is a member of the group l 2 [0，∞]Ii is the sign of the identity matrix with dimensions i x i. 0 is a zero matrix with corresponding dimensions.
The design principle of the observer is to determine
So that the error dynamic model pattern (9) meets the following two objectives:
(1)
is Hulvitz stable. The characteristic value of the discrete time system is in a unit circle;
(2) Error of fault estimation e f (k) For the following
Insensitivity, i.e f (k) The smaller the better.
As can be seen from the foregoing description, in order to detect the small changes hidden in the environmental noise in the fault characteristics, the method for detecting the short-circuit fault of the lithium battery provided in the embodiments of the present application combines the statistical information (accumulation sum) with the model-based observer fault estimation for the early diagnosis method of the short-circuit of the battery, specifically uses the battery short-circuit equivalent model and the battery fuzzy observer in combination, and determines that the fault detection method for the short-circuit fault of the battery has the characteristics of model universality, design universality and implementation universality according to the residual error of the actual value of the battery state of charge measured by the battery short-circuit equivalent model and the estimated value of the battery state of charge estimated by the fuzzy observer.
In one embodiment of the method for detecting a short-circuit fault of a lithium battery provided in the present application, the method for detecting a short-circuit fault of a lithium battery further includes:
modeling calculation is carried out on all charge states of a sample battery, and the battery short circuit equivalent model is obtained.
In the present embodiment, I is according to kirchhoff's circuit law in ＝I batt +I sc Is the actual input current to the battery, with or without a short circuit fault. If R is sc Approaching infinity, then I sc ≈0，I in ≈I batt Indicating that the battery is operating well. Otherwise, internal short circuit or external short circuit fault occurs. SOC of battery E [0%,100%]The battery short circuit equivalent model can be obtained through modeling calculation by a classical coulomb counting method.
In an embodiment of a method for detecting a short-circuit fault of a lithium battery provided in the present application, modeling calculation is performed on all states of charge of a sample battery to obtain the battery short-circuit equivalent model, including:
obtaining the charge state of the sample battery at the next moment according to the load current of the sample battery at the current moment and the initial charge state of the sample battery;
and establishing a battery short circuit equivalent model according to the state of charge of the sample battery at the current moment and the state of charge of the sample battery at the next moment.
In the present embodiment, as shown in FIG. 4, the average value V thereof OC The (SOC) is typically a nonlinear monotonically increasing function of the SOC. In each SOC interval, it may be approximated as
(a i And b i Constant during the ith SOC interval). V (V) 1 And V 2 Capacitance C respectively 1 And C 2 The voltage across it. V (V) batt Is the lithium ion battery terminal voltage. I batt Is the battery load current. According to the reference direction in fig. 2, "+" indicates a discharging process, and "-" indicates a charging process. I sc Is an SC current flowing into the equivalent SC resistor Rsc, the value of which is +.>
Integrating the load current of the sample battery at the current moment to obtain a current integral value; according to the initial stageAnd obtaining the initial state of charge and the current integral value to obtain the state of charge of the sample battery at the next moment, namely: SOC of battery E [0%,100%]The modeling calculation can be performed by a classical coulomb counting method:
where η is the charge-discharge efficiency, typically approximately 1; c (C) n Is the nominal capacity of the battery, and the unit is Ah; soc (t) is based on its initial value Soc (t) 0 ) Soc at time t.
Using kirchhoff's law, a battery dynamics model can be described by the following discrete state space representation:
wherein:
due to I in ＝I batt +I sc The battery equivalent circuit model with the short-circuit resistance in the i-th SOC interval is expressed as:
x(k+1)＝Ax(k)+BI batt (k)+B f I sc (k)
y(k)＝Cx(k)+DI batt (k)+D f I sc (k) (6)
Wherein B is f ＝B；C＝[-1 -1 a i ]；D＝D f ＝-R 0 . The state vector is x (k) = [ V 1 (k)，V 1 (k)，soc(k)]'. Model output is y (k) =v batt (k)-b i 。
The battery short-circuit model form (6) can be further extended to equation (7) taking modeling errors and measurement noise into account, where both the state equation and the output equation take into account bounded perturbations:
x(k+1)＝Ax(k)+B f f(k)+B d d(k)
y(k)＝Cx(k)+Du(k)+D f f(k)+D d d(k) (7)
wherein x (k) ∈R n Is a state vector; y (k) ∈R p Is the output; u (k) ∈R m Is a known input corresponding to I batt ；
Is a battery failure corresponding to I sc ；/>
Is of the type I 2 [0，∞]Is a disturbance of (2); b (B) d And D d Is a constant real matrix of appropriate dimensions.
In one embodiment of the method for detecting a short-circuit fault of a lithium battery provided in the present application, the method for detecting a short-circuit fault of a lithium battery further includes:
and obtaining the fuzzy observer according to the battery voltage charge state curve and a preset robust observer.
In this embodiment, a method of mixing the linear intervals of the OCV-SOC curves and constructing a weighting function self-adjusting fault estimator is presented. The creation of a TS fuzzy system involves modeling and observer/controller design processes to model a nonlinear system through a set of local LTI models. These models are interpolated using nonlinear weighting functions or membership functions, enabling fusion of all linear subsystems.
In an embodiment of a method for detecting a short-circuit fault of a lithium battery provided in the present application, the obtaining the fuzzy observer according to a battery voltage charge state curve and a preset robust observer includes:
Obtaining an optimal weighting function of the battery state of charge according to the battery voltage state of charge curve and a preset Gaussian function;
and obtaining the fuzzy observer according to the optimal weighting function and the robust observer.
In this embodiment, the overall design flow of the blurred observer is shown in fig. 5. First, blurring of the OCV-SOC curve is performed. Fig. 6 shows a blurring flow of the OCV-SOC curve of the battery.
Fig. 6 (b): consider g (g.epsilon.N and g.gtoreq.2) linear segments in the OCV-SOC curve and fit the curve for each linear segment to the form of equation (3). The determination of g depends on the non-linear level of the OCV-SOC curve.
With this approach, the linear segments need not be continuous. This approach has two main advantages: the model is simpler than classical models using small discrete steps; the model is robust because it does not require a strong approximation of the linearity of the entire curve.
Fig. 6 (c): membership functions are assigned to each linear segment, i.e., each linear segment is passed through a weighting function pi (SOC) to reflect the degree of influence on the global non-linear behavior of the OCV-SOC curve. Furthermore, pi is chosen as a gaussian-like function in order to smooth transitions between different linear segments. Thus, pi (Soc) is determined from its mean μ and variance σ 2 And (5) determining. For any value of soc, assume:
π i (soc) is not less than 0
Fig. 6 (d): after the two steps, the OCV-SOC curve can be expressed as:
wherein:
thus, for any SOC value, h i (Soc) satisfies:
h i (soc) is not less than 0
Fig. 6 (e): pi for each membership function i (i=1, 2,., g) select the optimum (μ, σ 2 )。
The fault estimator is designed using the following theorem:
theorem: let a designated H ∞ The expression level of (2) is gamma and gives a circular domain
If two symmetrical positive definite matrices are present +.>
Two matrices->
Satisfies the formulas (10) and (11):
when (when)
Representing the symmetric term of the matrix, followed by the error dynamics (9)
Wherein, the liquid crystal display device comprises a liquid crystal display device,
representing a matrix symmetry term. The error dynamic model type (9) satisfies H ∞ Expression index of->
Characteristic value is->
Gain matrix->
By->
And (5) determining. In short, the linear fault estimator design obeys two linear matrix inequalities. By adjusting the circle field->
Observer performance can be adjusted.
Thus, according to the previously proposed method, a robust observer is first established for each linear segment. That is, the gain vector of each sub-observer is determined
Then, based on equation (8), g linear robust observers can be directly mixed into equation (12), where the subscript (·) fuzzy represents the elements of the TS fuzzy observer.
Two important points should be pointed out. Firstly, according to a TS fuzzy system design method, the optimal MF of different linear observers is identical to the MF in the battery OCV-SOC curve fuzzification process [31 ]]. Secondly, the first step of the method comprises the steps of,
namely SSlow-varying properties of OC>
Wherein->
Included in the estimated state vector
Thus (S)>
Is a so-called fuzzy variable in the TS fuzzy observer of the battery equivalent circuit model, and the obtained TS fuzzy observer is a self-adjusting robust observer of a weighting function. The schematic diagram of the obtained TS fuzzy observer is shown in FIG. 7.
In an embodiment of the method for detecting a short-circuit fault of a lithium battery provided in the present application, the obtaining an optimal weighting function of a battery state of charge according to the battery voltage state of charge curve and a preset gaussian function includes:
obtaining an optimization coefficient of a Gaussian function according to the battery voltage charge state curve and a preset Gaussian function;
and according to the optimization coefficient, determining the optimal parameter of the Gaussian function, and further determining the optimal weighting function of the battery state of charge.
In the present embodiment, fig. 6 (e): pi for each membership function i (i=1, 2,., g) select the optimum (μ, σ 2 ). Determination coefficient R as optimization criterion 2 The calculation is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
n ocv is the number of data points used in the optimization process;V OC (SOC) is raw average OCV-SOC data as shown in fig. 6 (a). Finally, as shown in FIG. 6 (f), when R 2 Infinite approaching 1, the optimal (μ, σ) of the membership function is obtained 2 ) The optimal membership function obtained after the optimization process is used for fusion of the subsequent observers.
In a second aspect, in order to diagnose a thermal runaway fault of a battery of a large-scale energy storage power station in time, the application provides an embodiment of a lithium battery short-circuit fault detection system, referring to fig. 8, the lithium battery short-circuit fault detection system specifically includes the following contents:
parameter calculation module 01: inputting the charge states of all the current battery at all times into a preset battery short-circuit equivalent model, and outputting battery state parameters of the battery at the next time by the battery short-circuit equivalent model;
parameter estimation module 02: estimating battery state estimation parameters of a current lithium battery by adopting a fuzzy observer;
fault detection module 03: and determining the short circuit fault of the battery according to the battery state parameter and the residual error of the battery state estimation parameter.
In this embodiment, the parameter calculation module 01 is configured to establish a battery short-circuit equivalent model, obtain an actual value of a battery parameter through the battery short-circuit equivalent model, the parameter estimation module 02 is configured to design a fuzzy observer, obtain an estimated value of the battery parameter through the fuzzy observer, and the fault detection module 03 calculates a residual error between the actual value of the battery parameter of the parameter calculation module 01 and the estimated value of the battery parameter of the parameter estimation module 02, and determine a battery fault state through the residual error, where the residual error is a 1 battery fault, and the residual error is a 0 battery normal operation.
As can be seen from the foregoing description, in order to detect a small change hidden in environmental noise in a fault feature, the system for detecting a short-circuit fault of a lithium battery provided in the embodiments of the present application combines statistical information (accumulation sum) with model-based observer fault estimation, and is used in a method for early diagnosis of a short-circuit of a battery, specifically, a battery short-circuit equivalent model and a battery fuzzy observer are used in combination, and a battery short-circuit fault is determined according to a residual error between an actual value of a battery state of charge measured by the battery short-circuit equivalent model and an estimated value of the battery state of charge estimated by the fuzzy observer.
In order to diagnose out a thermal runaway fault of a battery of a large-scale energy storage power station in time from a hardware level, the application provides an embodiment of an electronic device with all or part of contents in a lithium battery short-circuit fault detection method, wherein the electronic device specifically comprises the following contents:
fig. 9 is a schematic block diagram of a system configuration of an electronic device 9600 of an embodiment of the present application. As shown in fig. 9, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 9 is exemplary; other types of structures may also be used in addition to or in place of the structures to implement telecommunications functions or other functions.
In one embodiment, the lithium battery short-circuit fault detection function may be integrated into the central processor. Wherein the central processor may be configured to control:
step 100: inputting the charge states of all the current battery at all times into a preset battery short-circuit equivalent model, and outputting battery state parameters of the battery at the next time by the battery short-circuit equivalent model;
step 200: estimating battery state estimation parameters of a current lithium battery by adopting a fuzzy observer;
step 300: and determining the short circuit fault of the battery according to the battery state parameter and the residual error of the battery state estimation parameter.
In the present embodiment, considering the slow variation characteristic of the battery SOC, a systematic method is proposed based on a genetic algorithm to construct a self-regulating mechanism to cope with a nonlinear Open Circuit Voltage (OCV) -SOC curve. In order to detect the tiny change hidden in the environmental noise in the fault characteristics, the statistical information (accumulated sum) is combined with the fault estimation of the observer based on the model, the whole strategy architecture is shown in fig. 2, a battery short circuit equivalent model is established first, and then the OCV-SOC curve and the robust observer are subjected to fuzzification to obtain the fuzzy observer.
The fuzzy observer is essentially a weighted function self-adjusting robust observer, and the battery parameter estimation value of the battery is obtained through the fuzzy observer, and the battery short circuit equivalent model is shown in figure 3. Resistor R 0 Represents an ohmic resistance that includes the resistance of the contacts, electrodes, and electrolyte. The double RC loop characterizes the charge transfer effect, diffusion effect and double-layer behavior inside the lithium ion battery, and can simulate the transient response of the battery. Furthermore, the dual RC network is a good tradeoff between model error and model complexity compared to single RC and triple RC structures. And obtaining the actual value of the battery parameter of the battery through the battery short circuit equivalent model, judging whether the battery is in a fault state or not through the residual error of the actual value and the estimated value of the battery parameter, wherein the battery normally operates if the residual error is 0, and the battery is in a short circuit fault if the residual error is 1.
Due to i in ＝ batt + sc The battery equivalent circuit model with the short-circuit resistance in the i-th SOC interval is expressed as: x (k+1) =ax (k) +b f f(k)+B d d(k)
y(k)＝Cx(k)+Du(k)+D f f(k)+D d d(k) (7)
Wherein x (k) ∈R n Is a state vector; y (k) ∈R p Is the output; u (k) ∈R m Is a known input corresponding to I batt ；
Is a battery failure corresponding to I sc ；/>
Is of the type I 2 [0，∞]Is a disturbance of (2); b (B) d And D d Is a constant real matrix of appropriate dimensions.
The fault estimator is designed as a proportional-integral observer. The integral term not only can ensure the robust state estimation when the fault occurs, but also can simultaneously provide the fault estimation, and the fuzzy observer can be expressed as:
Wherein, the liquid crystal display device comprises a liquid crystal display device,
is an estimated state vector; />
Is the observer output; />
Is an estimated f (k); l epsilon R n×p And; />
Is the observer gain.
The error dynamics between model expression (7) and observer expression (8) can be described as equation (9):
wherein:
and is also provided with
Δf (k) =f (k+1) -f (k) is a member of the group l 2 [0，∞]Ii is the sign of the identity matrix with dimensions i x i. 0 is a zero matrix with corresponding dimensions.
The design principle of the observer is to determine
So that the error dynamic model pattern (9) meets the following two objectives:
(1)
is Hulvitz stable. The characteristic value of the discrete time system is in a unit circle;
(2) Error of fault estimation e f (k) For the following
Insensitivity, i.e f (k) The smaller the better.
As can be seen from the foregoing description, in order to detect a small change hidden in environmental noise in a fault feature, the electronic device provided in the embodiment of the present application combines statistical information (cumulative sum) with a model-based observer fault estimation for use in a battery short-circuit early diagnosis method, specifically, uses a battery short-circuit equivalent model in combination with a battery fuzzy observer, and determines a battery short-circuit fault according to a residual error between an actual value of a battery state of charge measured by the battery short-circuit equivalent model and an estimated value of the battery state of charge estimated by the fuzzy observer.
In another embodiment, the lithium battery short-circuit fault detection device may be configured separately from the central processor 9100, for example, the lithium battery short-circuit fault detection device may be configured as a chip connected to the central processor 9100, and the lithium battery short-circuit fault detection function is implemented by control of the central processor.
As shown in fig. 9, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 need not include all of the components shown in fig. 9; in addition, the electronic device 9600 may further include components not shown in fig. 9, and reference may be made to the related art.
As shown in fig. 9, the central processor 9100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 9100 receives inputs and controls the operation of the various components of the electronic device 9600.
The memory 9140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information about failure may be stored, and a program for executing the information may be stored. And the central processor 9100 can execute the program stored in the memory 9140 to realize information storage or processing, and the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.
The memory 9140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, etc. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 storing application programs and function programs or a flow for executing operations of the electronic device 9600 by the central processor 9100.
The memory 9140 may also include a data store 9143, the data store 9143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, address book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. A communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, as in the case of conventional mobile communication terminals.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, etc., may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132 to implement usual telecommunications functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100 so that sound can be recorded locally through the microphone 9132 and sound stored locally can be played through the speaker 9131.
The embodiments of the present application further provide a computer readable storage medium capable of implementing all the steps in the method for detecting a short-circuit fault of a lithium battery in the above embodiments, where the computer readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements all the steps in the method for detecting a short-circuit fault of a lithium battery in which an execution subject in the above embodiments is a server or a client, for example, the processor implements the following steps when executing the computer program:
Step 100: inputting the charge states of all the current battery at all times into a preset battery short-circuit equivalent model, and outputting battery state parameters of the battery at the next time by the battery short-circuit equivalent model;
step 200: estimating battery state estimation parameters of a current lithium battery by adopting a fuzzy observer;
step 300: and determining the short circuit fault of the battery according to the battery state parameter and the residual error of the battery state estimation parameter.
In the present embodiment, considering the slow variation characteristic of the battery SOC, a systematic method is proposed based on a genetic algorithm to construct a self-regulating mechanism to cope with a nonlinear Open Circuit Voltage (OCV) -SOC curve. In order to detect the tiny change hidden in the environmental noise in the fault characteristics, the statistical information (accumulated sum) is combined with the fault estimation of the observer based on the model, the whole strategy architecture is shown in fig. 2, a battery short circuit equivalent model is established first, and then the OCV-SOC curve and the robust observer are subjected to fuzzification to obtain the fuzzy observer.
The fuzzy observer is essentially a weighted function self-adjusting robust observer, and the battery parameter estimation value of the battery is obtained through the fuzzy observer, and the battery short circuit equivalent model is shown in figure 3. Resistor R 0 Represents an ohmic resistance that includes the resistance of the contacts, electrodes, and electrolyte. The double RC loop characterizes the charge transfer effect, diffusion effect and double-layer behavior inside the lithium ion battery, and can simulate the transient response of the battery. Furthermore, the dual RC network is a good tradeoff between model error and model complexity compared to single RC and triple RC structures. And obtaining the actual value of the battery parameter of the battery through the battery short circuit equivalent model, judging whether the battery is in a fault state or not through the residual error of the actual value and the estimated value of the battery parameter, wherein the battery normally operates if the residual error is 0, and the battery is in a short circuit fault if the residual error is 1.
Due to I in ＝I batt +I sc The battery equivalent circuit model with the short-circuit resistance in the i-th SOC interval is expressed as: x (k+1) =ax (k) +b f f(k)+B d d(k)
y(k)＝Cx(k)+Du(k)+D f f(k)+D d d(k) (7)
Wherein x (k) ∈R n Is a state vector; y (k) ∈R p Is the output; u (k) ∈R m Is a known input corresponding to I batt ；
Is a battery failure corresponding to I sc ；/>
Is of the type I 2 [0，∞]Is a disturbance of (2); b (B) d And D d Is a constant real matrix of appropriate dimensions.
The fault estimator is designed as a proportional-integral observer. The integral term not only can ensure the robust state estimation when the fault occurs, but also can simultaneously provide the fault estimation, and the fuzzy observer can be expressed as:
Wherein, the liquid crystal display device comprises a liquid crystal display device,
is an estimated state vector; />
Is the observer output; />
Is an estimated f (k); l epsilon R n×p And; />
Is the observer gain.
The error dynamics between model expression (7) and observer expression (8) can be described as equation (9):
wherein:
and is also provided with
Δf (k) =f (k+1) -f (k) is a member of the group l 2 [0，∞]Ii is the sign of the identity matrix with dimensions i x i. 0 is a zero matrix with corresponding dimensions.
The design principle of the observer is to determine
So that the error dynamic model pattern (9) meets the following two objectives:
(1)
is Hulvitz stable. The characteristic value of the discrete time system is in a unit circle;
(2) Error of fault estimation e f (k) For the following
Insensitivity, i.e f (k) The smaller the better.
As can be seen from the foregoing description, in order to detect a small change hidden in environmental noise in a fault feature, the computer readable storage medium provided in the embodiments of the present application combines statistical information (accumulated sum) with model-based observer fault estimation for use in a battery short-circuit early diagnosis method, specifically, uses a battery short-circuit equivalent model and a battery fuzzy observer in combination, and determines a battery short-circuit fault according to a residual error between an actual value of a battery state of charge measured by the battery short-circuit equivalent model and an estimated value of the battery state of charge estimated by the fuzzy observer.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (10)
1. The method for detecting the short-circuit fault of the lithium battery is characterized by comprising the following steps of:
inputting the charge states of all the current battery at all times into a preset battery short-circuit equivalent model, and outputting battery state parameters of the battery at the next time by the battery short-circuit equivalent model;
estimating battery state estimation parameters of a current lithium battery by adopting a fuzzy observer;
and determining the short circuit fault of the battery according to the battery state parameter and the residual error of the battery state estimation parameter.
2. The method for detecting a short-circuit fault of a lithium battery according to claim 1, further comprising:
modeling calculation is carried out on all charge states of a sample battery, and the battery short circuit equivalent model is obtained.
3. The method for detecting a short-circuit fault of a lithium battery according to claim 1, wherein the modeling calculation is performed for all states of charge of a sample battery to obtain the battery short-circuit equivalent model, comprising:
obtaining the charge state of the sample battery at the next moment according to the load current of the sample battery at the current moment and the initial charge state of the sample battery;
And establishing a battery short circuit equivalent model according to the state of charge of the sample battery at the current moment and the state of charge of the sample battery at the next moment.
4. The method for detecting a short-circuit fault of a lithium battery according to claim 2, wherein the obtaining the state of charge of the sample battery at the next moment according to the load current of the sample battery at the current moment and the initial state of charge of the sample battery comprises:
integrating the load current of the sample battery at the current moment to obtain a current integral value;
and obtaining the state of charge of the sample battery at the next moment according to the initial state of charge and the current integral value.
5. The method for detecting a short-circuit fault of a lithium battery according to claim 2, further comprising:
and obtaining the fuzzy observer according to the battery voltage charge state curve and a preset robust observer.
6. The method for detecting a short-circuit fault of a lithium battery according to claim 5, wherein the obtaining the fuzzy observer according to a battery voltage state-of-charge curve and a preset robust observer comprises:
obtaining an optimal weighting function of the battery state of charge according to the battery voltage state of charge curve and a preset Gaussian function;
And obtaining the fuzzy observer according to the optimal weighting function and the robust observer.
7. The method for detecting a short circuit fault of a lithium battery according to claim 6, wherein the obtaining an optimal weighting function of the state of charge of the battery according to the state of charge curve of the battery voltage and a preset gaussian function comprises:
obtaining an optimization coefficient of a Gaussian function according to the battery voltage charge state curve and a preset Gaussian function;
and according to the optimization coefficient, determining the optimal parameter of the Gaussian function, and further determining the optimal weighting function of the battery state of charge.
8. A lithium battery short-circuit fault detection system, comprising:
parameter calculation module: inputting the charge states of all the current battery at all times into a preset battery short-circuit equivalent model, and outputting battery state parameters of the battery at the next time by the battery short-circuit equivalent model;
parameter estimation module: estimating battery state estimation parameters of a current lithium battery by adopting a fuzzy observer;
and a fault detection module: and determining the short circuit fault of the battery according to the battery state parameter and the residual error of the battery state estimation parameter.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method for detecting a short circuit fault of a lithium battery according to any one of claims 1 to 7 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the lithium battery short-circuit fault detection method according to any one of claims 1 to 7.
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| CN116540125A (en) * | 2023-07-05 | 2023-08-04 | 中国华能集团清洁能源技术研究院有限公司 | Diagnosis method and system for battery state-of-charge estimation fault |
| CN116540125B (en) * | 2023-07-05 | 2023-10-03 | 中国华能集团清洁能源技术研究院有限公司 | Diagnosis method and system for battery state-of-charge estimation fault |
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