CN117148173A - Battery sensor fault diagnosis method and device and electronic equipment - Google Patents
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Abstract
The application relates to the technical field of battery fault diagnosis, in particular to a battery sensor fault diagnosis method, a device and electronic equipment, wherein the method comprises the following steps: constructing a set value observer model based on the state predictor and the state estimator; performing fault detection on the battery sensor based on the set value observer model to obtain a fault detection result; and based on the fault detection result, performing fault identification by adopting a double-layer Pearson correlation coefficient to obtain the source and type of the sensor fault. By means of a set value observer comprising a state predictor and a state estimator, so as to ensure that the unavailable actual battery state caused by unknown modeling errors and noise at each moment is included, fault detection is achieved based on the intersection between a prediction ellipsoid and an estimation ellipsoid, the source and the type of a sensor fault are identified by utilizing a double-layer Pearson correlation coefficient analysis mechanism, and efficient and accurate battery sensor fault diagnosis is achieved.
Description
Technical Field
The present application relates to the field of battery fault diagnosis technologies, and in particular, to a method and an apparatus for diagnosing a battery sensor fault, and an electronic device.
Background
In energy storage systems, the battery acts as the primary energy storage and release device, and its performance and reliability directly impact the efficiency and usability of the overall system. Failure of the battery may result in a decrease in the energy density and capacity loss of the system, thereby reducing the energy storage efficiency and available energy capacity of the system. In addition, the safety problems of overcharge, overdischarge, internal resistance increase and the like may be caused, so that serious accidents such as thermal runaway, explosion or fire disaster and the like of the battery system may occur. Therefore, the method has important significance for timely and accurately diagnosing the battery faults. Through the battery fault diagnosis technology, parameters (such as current, voltage, temperature and the like) of the battery can be monitored in real time, potential fault characteristics are identified, and measures can be taken in time to repair or replace the fault battery.
The existing battery sensor fault diagnosis methods can be divided into two types: model-free methods and model-based methods. Model-free methods typically perform fault diagnostics based on cross-voltage measurements and correlation coefficient analysis, however, such methods do not allow an estimate of the degree of sensor failure. The modeling method diagnoses whether the sensor is malfunctioning through filter estimation and prediction of the SOC, and most of the modeling methods ignore inherent nonlinearities between the SOC and battery system parameters, limiting the applicability of these methods in battery systems, and limiting their accuracy of estimation in sensor failure severity assessment.
It can be seen from the above that how to design an efficient and accurate fault diagnosis method for a battery sensor is a problem to be solved at present.
Disclosure of Invention
The present application aims to solve at least one of the technical problems in the related art to some extent.
Therefore, a first object of the present application is to provide a fault diagnosis method for a battery sensor, so as to solve the problems of low accuracy, poor efficiency and the like in the prior art.
A second object of the application is to propose a device.
A third object of the present application is to propose an electronic device.
A fourth object of the present application is to propose a computer readable storage medium.
To achieve the above object, an embodiment of a first aspect of the present application provides a battery sensor fault diagnosis method, including:
constructing a set value observer model based on the state predictor and the state estimator;
performing fault detection on the battery sensor based on the set value observer model to obtain a fault detection result;
and based on the fault detection result, performing fault identification by adopting a double-layer Pearson correlation coefficient to obtain the source and type of the sensor fault.
Preferably, the constructing a set value observer model based on the state predictor and the state estimator comprises:
constructing a battery circuit model based on the equivalent circuit model;
discretizing and linearly processing the battery circuit model by utilizing a coulomb counting method to obtain a modeling error;
a set-valued observer model is constructed comprising a state predictor and a state estimator based on the modeling error.
Preferably, the battery circuit model expression is:
wherein,and->For the voltage across RC and the battery terminal voltage, < >>Internal resistance of->In the event of an open circuit voltage source,，/>for polarizing resistive and capacitive networks, < >>Is an internal current.
Preferably, the discretizing and linearity processing are performed on the battery circuit model by using a coulomb counting method, and obtaining the modeling error includes:
discretizing the battery circuit model based on a coulomb counting method to obtain a discrete battery circuit model;
and linearly processing the discrete battery circuit by utilizing a Taylor series to obtain a modeling error.
Preferably, the state predictor expression is:
the state estimator expression is:
wherein,to +.>Disabled battery status at->Prediction of->To be at the time ofkDisabled battery state at->Estimate of->At time->Battery status where no use is possible->Estimate of->And->Is the estimator gain, +.>Predicted battery terminal voltage, +.>Is the prediction period gain.
Preferably, the fault detection of the battery sensor based on the set value observer model includes:
rewriting the set value observer model based on battery sensor fault data to obtain an augmented observer model;
performing linear processing on the augmented observer model based on modeling errors and noise to obtain an augmented observer model;
and performing fault detection by using the enhanced observer model battery sensor to obtain a fault detection result.
Preferably, the linear processing expression of the augmented observer model based on modeling error and noise is:
wherein,is in an augmented state, is->Is->The second element of the vector, matrix A, B, C, D, is a coefficient matrix of the state space equation discretized according to the battery circuit model,/->For system input, ++>For system output, ++>For battery->。
To achieve the above object, a second aspect of the present application provides a battery sensor failure diagnosis apparatus, comprising:
an observer construction module that constructs a set value observer model based on the state predictor and the state estimator;
the fault detection module is used for carrying out fault detection on the battery sensor based on the set value observer model to obtain a fault detection result;
and the identification module is used for carrying out fault identification by adopting a double-layer Pearson correlation coefficient based on the fault detection result to obtain the source and the type of the sensor fault.
To achieve the above object, an embodiment of a third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of the above.
To achieve the above object, an embodiment of a fourth aspect of the present application provides a computer-readable storage medium, including computer-executable instructions stored in the computer-readable storage medium, the computer-executable instructions when executed by a processor being configured to implement the method of any one of the above.
According to the battery sensor fault diagnosis method provided by the application, unknown modeling errors, unknown processes and unknown measurement noise are established to describe battery electrical behaviors. Only the hard boundaries of modeling errors and noise are required to be unknown, and no prior knowledge of their statistical properties is required, the battery state being reconstructed by a set of value observers. The state prediction and state estimation of each time step of the predictor and the estimator are some ellipsoid sets in the state space, are not influenced by unknown modeling errors and noise, and improve the efficiency and the accuracy of fault diagnosis of the battery sensor.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
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The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flowchart of a first embodiment of a battery sensor fault diagnosis method according to the present application;
FIG. 2 is a battery equivalent circuit model;
fig. 3 is a block diagram of a battery sensor fault diagnosis device according to an embodiment of the present application.
Detailed Description
The application provides a battery sensor fault diagnosis method, a device and electronic equipment, wherein the battery state is reconstructed through a value collecting observer, the detection precision is improved, and the simultaneous performance of fault detection, fault identification and fault estimation is realized by utilizing a unified sensor fault diagnosis algorithm.
In order to better understand the aspects of the present application, the present application will be described in further detail with reference to the accompanying drawings and detailed description. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, fig. 1 is a flowchart of a first embodiment of a battery sensor fault diagnosis method according to the present application; the specific operation steps are as follows:
step S101: constructing a set value observer model based on the state predictor and the state estimator;
constructing a battery circuit model based on the equivalent circuit model;
discretizing and linearly processing the battery circuit model by utilizing a coulomb counting method to obtain a modeling error;
discretizing the battery circuit model based on a coulomb counting method to obtain a discrete battery circuit model;
and linearly processing the discrete battery circuit by utilizing a Taylor series to obtain a modeling error.
Constructing a set value observer model comprising a state predictor and a state estimator based on the modeling error;
the battery circuit model expression is:
wherein,and->For the voltage across RC and the battery terminal voltage, < >>Internal resistance of->In the event of an open circuit voltage source,，/>for polarizing resistive and capacitive networks, < >>Is an internal current;
the state predictor expression is:
the state estimator expression is:
wherein,to +.>Disabled battery status at->Prediction of->To be at the time ofkDisabled battery state at->Estimate of->At time->Battery status where no use is possible->Estimate of->And->Is the estimator gain, +.>Predicted battery terminal voltage, +.>Is the prediction period gain.
Step S102: performing fault detection on the battery sensor based on the set value observer model to obtain a fault detection result;
rewriting the set value observer model based on battery sensor fault data to obtain an augmented observer model;
performing linear processing on the augmented observer model based on modeling errors and noise to obtain an augmented observer model;
the linear processing expression of the augmented observer model based on modeling error and noise is as follows:
wherein, among them,is in an augmented state, is->Is->The second element of the vector, matrix A, B, C, D, is the coefficient of the state space equation after discretization according to the battery circuit modelMatrix (S)>For system input, ++>For system output, ++>Is a battery。
And performing fault detection by using the enhanced observer model battery sensor to obtain a fault detection result.
Step S103: and based on the fault detection result, performing fault identification by adopting a double-layer Pearson correlation coefficient to obtain the source and type of the sensor fault.
The present embodiment provides a battery sensor fault diagnosis method, which includes a state predictor and a state estimator, so as to ensure that the unavailable actual battery state caused by unknown modeling errors and noise at each moment is included, the estimated and predicted battery state at each time step is an ellipsoid set in a state space instead of a single vector, fault detection is realized based on the intersection between a predicted ellipsoid and an estimated ellipsoid, and a double-layer pearson correlation coefficient analysis mechanism is utilized to identify the source and the type of a sensor fault, so that efficient and accurate battery sensor fault diagnosis is realized.
Based on the above embodiments, the present embodiment describes the battery sensor fault diagnosis method, specifically as follows:
step 1: designing a set value observer;
establishing a battery circuit model
The battery's electrical dynamics are described using the davidian battery equivalent circuit model as shown in fig. 2. The model consists of an open circuit voltage source having a nonlinear relationship with the SOCInternal resistance->And a pair of polarized resistor and capacitor networks +.>Composition is prepared. />Representing the internal current, positive discharge, negative charge,/->And->The voltage across the RC and the battery terminal voltage are represented, respectively. According to kirchhoff's law, the following battery model can be obtained:
(1)
according to the coulomb counting method,calculated as +.>. Discretizing the above to sample length +.>A battery model of seconds, and further expressed as a state space form:
(2)
(3)
wherein,is in a battery state->Is->，/>Is->。Is a system input,/->Represents the open-cell voltage, wherein the open-cell voltage is equal to the state of charge of the battery>Related functions, ++>Indicates the time sequence number +.>Is output by the system. The coefficient matrices of the formulas (2) and (3) obtained according to the formula (1) are:
to deal with non-linear parameters related to SOCAnd->The taylor series expansion is applied thereto to linearize it, and the formulas (2) and (3) become:
(4)
(5)
wherein,represents the estimated SOC at time k, +.>Is indicated at +.>Predicted SOC at:
specifically, in (4) and (5),unknown modeling error representing the linearization process described above, < >>Indicating influence on battery status->Unknown process noise, < >>Indicate destruction->Unknown measurement noise of (a). These errors and noise are assumed to be unknown but bounded and limited to the following ellipsoids:
(6)
wherein,respectively a shape matrix of a set of ellipsoids.
Designing a set value observer comprising a state predictor and a state estimator;
due to unknown but bounded modeling errorsProcess noise->And measuring noise->Is present in each time step +.>One-step forward real battery system status +.>Is inaccessible. To this end, we developed a set value observer to reconstruct the battery state, which can be further used to implement fault diagnostics. Specifically, the set value observer consists of two parts: a state predictor, set value estimator, as follows:
state predictor:
(7)
(8)
state estimator:
(9)
wherein,representing predicted battery terminal voltage,/->Is indicated at +.>Disabled battery status at->Prediction of->Representing the unusable battery state at time k +.>Estimate of->Is indicated at +.>Battery status where no use is possible->Estimate of->Is the predictor gain to be designed, +.>Andis the estimator gain to be designed.
Observer design aims at solving gain matrices，/>And->So that +/at each time step can be made>Position determination prediction->And estimate->. In this case, the +.>And->Only respectively express +.>Battery status at time->And a single estimate vector. The object of the application is not to determine a point-by-point prediction and estimation of the state of the unavailable battery at each time step, but rather to +_ at each time step>The following two ellipsoids were found:
wherein,and->Respectively representing two real valued positive shape matrices to be designed.
Assume at time k that the true battery system stateFalls into estimated ellipsoids +.>Wherein->Is a known center,/->Is a given shape matrix, if a matrix is present +.>And scalar quantityThe following optimization problem can be implemented:
(10)
wherein:
wherein,，/>is a unitary matrix->From factorization, modeling error defined in equation (6)>And noise->Next, the true battery status of one-step progression +.>Always at +.>Predicted ellipsoid of optimal state>Surround->、/>Respectively a shape matrix of a set of ellipsoids.
Suppose at timeTrue battery system status->Exist in state prediction ellipsoidIn (1)/(2)>Is a known center,/->Is given byIs a shape matrix of (1), if a matrix is present->，/>，/>And scalar->The following optimization problem can be implemented:
(11)
wherein:
wherein,，/>from factorization, then the noise defined in equation (6)The true battery status of the next step forward +.>Always at +.>Ellipsoid predicted by optimal stateSurround (S)>Is a shape matrix of a collection of ellipsoids.
It can be clearly seen that in (10) and (11), the set value observer design problem of the battery system is converted into an optimization problem of a set of recursive linear matrix inequalities, from which the predictor and estimator gain matrices and shape matrices of the optimal ellipsoid can be recursively determined. The proposed set-valued ellipsoidal observer design approach reduces the inherent unknown but bounded modeling errors and (process and measurement) noise, as the above design criteria include error and noise bound information. Therefore, the design method of the set value observer has certain elasticity and robustness to modeling errors and noise. Furthermore, the state predictors and estimators designed eliminate the rigorous requirement of the prevailing assumptions on the exact linearity of the battery system model. Another significant feature of the proposed observer design method is that in the presence of modeling errors and noise, its confidence and assurance is made at each time stepComprising unknown real battery system status->. This is because the calculated state predictions and estimates are sets of ellipsoids in the state space, rather than by some conventional estimation methods (e.g.)>Observer and kalman filter).
Step 2: implementing fault detection based on an intersection between the predicted ellipsoid and the estimated ellipsoid;
considering that the battery is at sensor failureIn (3), the battery models (2) and (3) become:
(12)
(13)
wherein,，/>determined by the sensor fault type set forth in table 1.
Table 1: enhancing battery model parameters
To estimate unknown sensor faultsWe rewrite the state space model described above into an augmented form as follows:
(14)
(15)
wherein,indicating that the state of the battery is enhanced,. The system matrix is described as:
similar to the formulas (4) and (5), inThe state space formula for the enhanced battery model form (14) can be expressed as:
(16)
(17)
wherein:
to estimate the augmented stateThe following set value observer was constructed:
(18)
(19)
wherein,is the observer gain to be designed. The purpose is to +/every time step>Determining an ellipsoid estimation set:
wherein,is a real valued positive shape matrix to be specified.
If a matrix is presentAnd scalar->The following optimization problems are made feasible:
(20)
wherein:
wherein,from factorization->Even in UBB the error and noise are modeled +.>And->In the case of (a) the real battery state of one-step progression +.>At time->Ellipsoid always estimated by optimal stateSurrounding.
Step 3: a two-layer pearson correlation coefficient analysis mechanism is employed to identify the source and type of sensor failure.
The present embodiment provides a sensor fault diagnosis algorithm that calculates the gain matrix of the required set value observer for state prediction and estimation, as shown in table 2. It is further shown that the algorithm can achieve comprehensive fault diagnosis, including fault detection, fault source and type identification, and sensor fault estimation. The basic principle of the sensor fault detection mechanism in the sensor fault diagnosis algorithm is to use some predefined threshold valuesAnd verifying the central deviation of the predicted and estimated ellipsoids. This is because due to the +.>Sensor failure, estimated center of ellipsoid +.>May suddenly change to predict the center of the ellipsoid +.>Remains in a healthy state because the previous state estimate is normal. As a result, at time->The two ellipsoids may have sufficient center offset, or even have empty intersections. In this sense, the detection criterion +.>To effectively detect the occurrence of a sensor failure.
To further identify the source and type of the fault, a two-layer correlation coefficient analysis mechanism is designed. Specifically, the correlation coefficient analysis mechanism is first applied to the SOC difference and the sampling time index. The current bias fault and the voltage scaling fault can be identified directly after the first layer analysis, and the voltage bias fault and the current scaling fault are causedBarriers generally have similar responses. Therefore, there is a need to provide a difference in SOC and a terminal voltage +.>Further analysis, i.e., a second layer analysis, is performed therebetween to complete successful identification.
Under such two-layer correlation coefficient analysis, four types of sensor faults can be effectively distinguished, including voltage sensor bias and scaling, current sensor bias and scaling.
As a final step of the diagnostic algorithm, the augmented set value observers in the form of (18) and (19) are designed to estimate both sensor faults and battery status after the sensor fault type and fault source identification steps.
TABLE 2
/>
According to the battery sensor fault diagnosis method provided by the embodiment of the application, unknown modeling errors, unknown processes and unknown measurement noise are established to describe battery electrical behaviors. Only the hard boundaries of modeling errors and noise are unknown, and no prior knowledge of their statistical properties is required, using a set-value observer to reconstruct the battery state. A significant feature of the designed predictor and estimator is that the state predictions and state estimates for each time step are some set of ellipsoids in the state space that guarantee to contain real battery system states that are not available and are not affected by unknown modeling errors and noise. Conventional filters or observers provide point-wise estimation from only a single vector at each time step. Through a unified sensor fault diagnosis algorithm, the simultaneous performance of fault detection, fault identification and fault estimation is realized. Unlike the existing residual error comparison method, fault detection is realized based on detecting the intersection of two recursively calculated prediction ellipsoid sets and an estimated ellipsoid set. Then, a double-layer pearson correlation coefficient analysis mechanism for fault source and type identification is proposed. Once a sensor fault is detected and identified, another set-value state observer is further constructed and designed to estimate the fault value and level for each time step, enabling efficient, accurate battery sensor fault diagnosis.
Referring to fig. 3, fig. 3 is a block diagram illustrating a fault diagnosis apparatus for a battery sensor according to an embodiment of the present application; the specific apparatus may include:
an observer construction module 100 that constructs a set value observer model based on the state predictor and the state estimator;
the fault detection module 200 is used for carrying out fault detection on the battery sensor based on the set value observer model to obtain a fault detection result;
and the identification module 300 is used for carrying out fault identification by adopting a double-layer pearson correlation coefficient based on the fault detection result to obtain the source and the type of the sensor fault.
A battery sensor fault diagnosis apparatus of the present embodiment is used to implement a battery sensor fault diagnosis method as described above, and thus, the detailed description of the embodiment of the battery sensor fault diagnosis apparatus may be found in the foregoing example portions of a battery sensor fault diagnosis method, for example, the observer building module 100, the fault detection module 200, and the identification module 300, which are respectively used to implement steps S101, S102, and S103 in the foregoing battery sensor fault diagnosis method, so, the detailed description thereof may be referred to the corresponding description of each portion of examples, and will not be repeated herein.
In order to achieve the above embodiment, the present application further provides an electronic device, including: a processor, and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the methods provided by the previous embodiments.
In order to implement the above-described embodiments, the present application also proposes a computer-readable storage medium having stored therein computer-executable instructions that, when executed by a processor, are adapted to implement the methods provided by the foregoing embodiments.
In order to implement the above embodiments, the present application also proposes a computer program product comprising a computer program which, when executed by a processor, implements the method provided by the above embodiments.
The processing of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user in the application accords with the regulations of related laws and regulations and does not violate the popular regulations of the public order.
It should be noted that personal information from users should be collected for legitimate and reasonable uses and not shared or sold outside of these legitimate uses. In addition, such collection/sharing should be performed after receiving user informed consent, including but not limited to informing the user to read user agreements/user notifications and signing agreements/authorizations including authorization-related user information before the user uses the functionality. In addition, any necessary steps are taken to safeguard and ensure access to such personal information data and to ensure that other persons having access to the personal information data adhere to their privacy policies and procedures.
The present application contemplates embodiments that may provide a user with selective prevention of use or access to personal information data. That is, the present disclosure contemplates that hardware and/or software may be provided to prevent or block access to such personal information data. Once personal information data is no longer needed, risk can be minimized by limiting data collection and deleting data. In addition, personal identification is removed from such personal information, as applicable, to protect the privacy of the user.
In the foregoing description of embodiments, reference has been made to the terms "one embodiment," "some embodiments," "example," "a particular example," or "some examples," etc., meaning that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. A battery sensor failure diagnosis method, characterized by comprising:
constructing a set value observer model based on the state predictor and the state estimator;
performing fault detection on the battery sensor based on the set value observer model to obtain a fault detection result;
and based on the fault detection result, performing fault identification by adopting a double-layer Pearson correlation coefficient to obtain the source and type of the sensor fault.
2. The battery sensor fault diagnosis method according to claim 1, wherein the constructing a set value observer model based on the state predictor and the state estimator comprises:
constructing a battery circuit model based on the equivalent circuit model;
discretizing and linearly processing the battery circuit model by utilizing a coulomb counting method to obtain a modeling error;
a set-valued observer model is constructed comprising a state predictor and a state estimator based on the modeling error.
3. The battery sensor malfunction diagnosis method according to claim 2, wherein the battery circuit model expression is:
wherein,and->For the voltage across RC and the battery terminal voltage, < >>Internal resistance of->Is an open circuit voltage source, ">，/>For polarizing resistive and capacitive networks, < >>Is an internal current.
4. The battery sensor fault diagnosis method according to claim 3, wherein said discretizing and linearizing the battery circuit model using coulomb counting to obtain modeling errors comprises:
discretizing the battery circuit model based on a coulomb counting method to obtain a discrete battery circuit model;
and linearly processing the discrete battery circuit by utilizing a Taylor series to obtain a modeling error.
5. The battery sensor malfunction diagnosis method according to claim 4, wherein the state predictor expression is:
the state estimator expression is:
wherein,to +.>Disabled battery status at->Prediction of->To be at the time ofkDisabled battery state at->Estimate of->At time->Battery status where no use is possible->Is used for the estimation of (a),and->Is the estimator gain, +.>Predicted battery terminal voltage, +.>Is the prediction period gain.
6. The battery sensor fault diagnosis method according to claim 1, wherein the performing fault detection on the battery sensor based on the set value observer model, obtaining a fault detection result includes:
rewriting the set value observer model based on battery sensor fault data to obtain an augmented observer model;
performing linear processing on the augmented observer model based on modeling errors and noise to obtain an augmented observer model;
and performing fault detection by using the enhanced observer model battery sensor to obtain a fault detection result.
7. The battery sensor fault diagnosis method according to claim 6, wherein the linear processing expression of the augmented observer model based on modeling error and noise is:
wherein,is in an augmented state, is->Is->The second element of the vector, matrix A, B, C, D, is a coefficient matrix of the state space equation discretized according to the battery circuit model,/->Is tied in asInput system->For system output, ++>For battery->。
8. A battery sensor failure diagnosis apparatus, characterized by comprising:
an observer construction module that constructs a set value observer model based on the state predictor and the state estimator;
the fault detection module is used for carrying out fault detection on the battery sensor based on the set value observer model to obtain a fault detection result;
and the identification module is used for carrying out fault identification by adopting a double-layer Pearson correlation coefficient based on the fault detection result to obtain the source and the type of the sensor fault.
9. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1-7.
10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-7.
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