Battery charging and discharging system fault diagnosis method based on multi-cell spatial filtering

Abstract

The invention discloses a battery charging and discharging system fault diagnosis method based on multi-cell spatial filtering, and belongs to the field of state estimation and fault detection, and the method comprises the steps: building a discrete state space model considering system time lag and fault, and then carrying out the state augmentation, and obtaining a discrete state space reconstruction model; a Lyapunov function and a # imgabs 0 # performance index are introduced to construct a # imgabs 1 # observer; performing multi-cell space expression on an augmented state estimation value output by the # imgabs2 # observer, and then constructing a target # imgabs3 # observer based on a multi-cell space Kalman filter and a real-time observation residual error; separating system state estimation and system fault estimation; and fault diagnosis is carried out after an envelope interval of system fault estimation is calculated. By constructing a triple filtering structure including state augmentation, ZKF optimization and dynamic residual error generation, the limitation of a traditional single-stage filter in a time delay system and fault detection is broken through, and the state estimation precision, the fault detection sensitivity and the system robustness are comprehensively improved.

Description

Technical Field

[0001] The invention relates to a battery charging and discharging system fault diagnosis method based on polyhedral space filtering, and belongs to the field of state estimation and fault detection. Background Art

[0002] With the rapid development of industrial automation and intelligence, fault detection technology has been widely used in power systems, aerospace, manufacturing, automotive engineering, and chemical industries, becoming an important means to ensure system safety and reliability. However, complex industrial systems are often accompanied by problems such as network transmission delays and equipment operation interference, which challenges the accuracy of system state estimation and fault detection.

[0003] Traditional state estimation and detection methods mainly include analytical model-based, signal processing-based, and knowledge-based detection methods. Among them, analytical model-based detection methods are widely used due to their high sensitivity. Classical algorithms include Kalman filters and particle filters. However, these methods usually assume that process noise and interference satisfy specific distribution laws, but non-random interference that is difficult to describe with statistical rules often occurs in actual systems, which leads to inaccurate or even invalid state estimation. In addition, traditional system modeling usually ignores the impact of system time lag. When the system time lag is significant, it may lead to inaccurate state estimation or even estimation divergence.

[0004] At present, state estimation and fault detection methods mainly include interval set inversion observer and polytope H Observer. Polytope H The observer can be found in “C. Li, F. Zhu, and S. Guo, "Interval estimation and fault detection for switched nonlinear systems based on zonotope method," Asian. J. Control, vol. 25, pp. 1420–1431, Sept. 2023.”. This method applies the polytope method to H The error dynamic system of the observer achieves accurate interval estimation of the system state in the absence of faults by iteratively calculating the minimum enclosing polyhedron of the system state vector. However, the residual it constructs contains interference, which makes its results less accurate than the method of this application and cannot be directly used for fault detection. Moreover, since this method does not fully consider the impact of system time delay on state estimation, there are large errors when dealing with systems containing time delays. Existing residual-based interval estimation methods can develop new fault detection strategies, but do not consider the state estimation and fault detection problems in time-delay systems.

[0005] In contrast, refer to the introduction in “Z. Wang, M. Zhang, Y. Wang, Y. Chen, and Z. Ji, "Guaranteed fault-estimation algorithm based on interval set inversion observer filtering, " International Journal of Control, Automation, and Systems, vol. 20, no. 11, pp. 3561–3572, Nov. 2022.” The interval set inversion observer designs a minimum conservative interval observer by minimizing the F-norm of the state error and shrinks the interval estimation results using vector Boolean operations and dimensional operations. This method performs well in dealing with unknown but bounded disturbances and noise in linear discrete-time systems, but faces some challenges. First, the computational complexity is high, especially in high-dimensional systems, where the computation time is long. Second, the memory requirement is large, especially when multiple binary divisions are performed, which poses a bottleneck for embedded systems. In addition, despite the use of the minimum conservative observer, the impact of system delay on state estimation is not fully considered, and the estimation is still too conservative in some cases, affecting the fault detection sensitivity. Summary of the invention

[0006] In order to solve at least one of the above problems, the present invention provides a battery charging and discharging system fault diagnosis method based on polyhedral space filtering, and the technical solution is as follows: As one aspect of the present invention, a battery charging and discharging system fault diagnosis method based on multi-cell spatial filtering is provided, comprising: For the battery charging and discharging system, a discrete state space model is constructed that takes into account the time delay and faults of the battery charging and discharging system. Perform state augmentation on the discrete state space model to obtain a discrete state space reconstruction model; For the discrete state space reconstruction model, construct Observer, and introduce Lyapunov Functions and Performance indicators, calculation The coefficient matrix of the observer; right The augmented state estimate of the battery charging and discharging system output by the observer is expressed in a polyhedral space; According to the multicellular space expression The observer, based on the polyhedral space Kalman filter, constructs the target by observing the residual in real time Observer, Target The boundary of the observer's state estimate polyhedral space is less than The state of the observer estimates the polyhedral space boundary, the target The output of the observer is the target augmented state estimate of the battery charging and discharging system; Separating the system state estimate and the system fault estimate from the target augmented state estimate, and calculating the envelope interval of the system fault estimate to obtain the upper and lower bounds of the envelope interval of the system fault estimate; According to the upper and lower bounds of the envelope interval of the system fault estimation, the fault occurrence of the battery charging and discharging system is analyzed to obtain the fault diagnosis result of the battery charging and discharging system.

[0007] Furthermore, the expression of the discrete state space model is:

[0008] in, express The status of the battery charging and discharging system at all times, express The status of the battery charging and discharging system at all times, , express The measurement output at the moment, , represents a known input, Indicates a fault in the battery charging and discharging system. , , A , , B , C , D , E and F Both represent coefficient matrices of given preset dimensions, h represents the known constant time lag, , , , , , , , , represents the unknown but bounded process noise, represents the unknown but bounded measurement noise, , , represents the set of real numbers, represents the set of positive integers, express A column vector of real elements, express A column vector of real elements, express A column vector of real elements, express A column vector of real elements, express A column vector of real elements, express A column vector of real elements, The dimension is The matrix of The dimension is The matrix of The dimension is The matrix of The dimension is The matrix of The dimension is The matrix of .

[0009] Furthermore, the discrete state space model is augmented to obtain a discrete state space reconstruction model, including: Introducing the augmented state vector , rewrite the above discrete state space model to obtain the discrete state space intermediate model. The expression of the discrete state space intermediate model is:

[0010] in, , , , , , , , express A column vector of real elements; Introducing the augmented state vector , rewrite the discrete state space intermediate model to obtain the discrete state space reconstruction model. The expression of the discrete state space reconstruction model is:

[0011] in, , , , , , , express A column vector of real elements, Represents the identity matrix.

[0012] Further, The expression of the observer is:

[0013] in, express The augmented state estimate at time , express The measurement output at the moment, S , J , L Both represent coefficient matrices, , , , , , , P represents the positive definite matrix in the Lyapunov function, W , Y All represent the coefficient matrices to be determined, λ , μ Both represent coefficient matrices, , , , , The dimension is The matrix of The dimension is The matrix of .

[0014] Further, The state estimation value of the battery charging and discharging system output by the observer is expressed in a polyhedral space, including: According to the given System status at all times and the coefficient matrix S , J , L ,get The expression of the augmented state estimate of the system state at time is:

[0015] in, , , Indicates package The coordinate vector of the center point of the polyhedron of the system state at the moment, Indicates package The shape matrix of the polyhedron of the system state at the moment, Indicates package The coordinate vector of the center point of the polyhedron of the system state at the moment, Indicates package The shape matrix of the polyhedron of the system state at the moment, represents the polytope generating matrix that encapsulates the measurement noise of the system, Represents the polytope generating matrix that encapsulates the process noise of the system.

[0016] Furthermore, the goal The expression of the observer is:

[0017] in, , , , represents the covariance in Kalman filtering, , and All represent intermediate parameters; In a given +1 time system status is hour, Time target The expression of the target augmented state estimate output by the observer is:

[0018] in, , Indicates package The target center point coordinate vector of the polyhedron of the system state at the moment, Indicates package The target shape matrix of the polyhedron of the system state at time instant, I represents the identity matrix, express Augment the state vector at any moment Estimated polytopic space.

[0019] Furthermore, the expression of system state estimation is:

[0020] in, Represents the separation based on the target augmented state estimate The system state estimate at time M Represents the augmented state vector The matrix of system state estimates extracted from , , express dimensional identity matrix, N Represents the augmented state vector The matrix of system fault estimates extracted from , , express dimensional identity matrix, RRepresents the augmented state vector The augmented state vector extracted from The matrix of , express dimensional identity matrix; The expression of system fault estimation is:

[0021] in, Represents the separation based on the target augmented state estimate k System failure estimation at the moment; The expression of the envelope interval of system fault estimation is:

[0022] in, represents the upper bound of the envelope interval of system fault estimation, represents the lower bound of the envelope interval of system fault estimation, , , Represents a polyhedral space Medium shape generation matrix No. All elements of the row Frobenius The sum of norms.

[0023] Furthermore, according to the upper and lower bounds of the envelope interval of the system fault estimation, the fault occurrence of the battery charging and discharging system is analyzed to obtain the fault diagnosis result of the battery charging and discharging system, including: if , make sure that there is no fault in the battery charging and discharging system; if or , determine that the battery charging and discharging system is faulty.

[0024] As another aspect of the present invention, a battery charging and discharging system fault diagnosis device based on polycellular spatial filtering is provided, which is used to implement the above-mentioned battery charging and discharging system fault diagnosis method based on polycellular spatial filtering, comprising: A model building module is used to build a discrete state space model for a battery charging and discharging system that takes into account the time delay and faults of the battery charging and discharging system; A model reconstruction module is used to perform state augmentation on the discrete state space model to obtain a discrete state space reconstruction model; The observer building module is used to reconstruct the model for the discrete state space and build Observer, and introduce Lyapunov Functions and Performance indicators, calculation The coefficient matrix of the observer; Multicellular spatial expression module for The augmented state estimate of the battery charging and discharging system output by the observer is expressed in a polyhedral space; Target The observer building block is used to represent the polyhedral space The observer, based on the polyhedral space Kalman filter, constructs the target by observing the residual in real time Observer, Target The boundary of the observer's state estimate polyhedral space is less than The state of the observer estimates the polyhedral space boundary, the target The output of the observer is the target augmented state estimate of the battery charging and discharging system; An envelope interval calculation module is used to separate the system state estimate and the system fault estimate from the augmented state estimate, and calculate the envelope interval of the system fault estimate to obtain the upper and lower bounds of the envelope interval of the system fault estimate; The judgment module is used to analyze the occurrence of the battery charging and discharging system faults according to the upper and lower bounds of the envelope interval estimated by the system fault, and obtain the fault diagnosis result of the battery charging and discharging system.

[0025] As another aspect of the present invention, a battery charging and discharging system is provided, comprising: The battery charging and discharging system body is used to control the battery charging and discharging process, manage and adjust the battery power, and make the battery work within the predetermined charging and discharging range; The battery charging and discharging system fault diagnosis device based on polyhedral spatial filtering is used to perform fault diagnosis on the battery charging and discharging system; The DC-DC circuit system is used to convert the input voltage to a predetermined battery charge and discharge voltage range.

[0026] The beneficial effects of the present invention are: The battery charging and discharging system fault diagnosis method based on polytopic space filtering provided by the present invention breaks through the limitations of traditional single-stage filters in time-delay systems and fault detection by constructing a triple collaborative filtering structure including state augmentation, polytopic space Kalman filter (Zonotopic Kalman Filter, ZKF) optimization and dynamic residual generation, and innovatively realizes the comprehensive and balanced optimization of state estimation accuracy, fault detection sensitivity and system robustness. The polytopic space Kalman filter, as the core, uses the Zonotope polytope set to describe the state space, and achieves a balance between the contraction of the estimation interval and the computational complexity through the Frobenius norm radius minimization criterion. Further introduction Performance indicators, design robustness-enhanced observers to suppress uncertainty interference, and combine state augmentation technology to effectively decouple time-delay and fault signals, while simplifying the observer design process. In order to improve the fault detection capability, a detection module based on a dynamic residual generator is also constructed, which combines the geometric characteristics of the Zonotope set to achieve high sensitivity and low missed detection rate. This optimizes the balance between robustness and accuracy of traditional filtering methods, and provides an efficient solution for state estimation and fault detection of complex time-delay systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 It is a brief flowchart of the battery charging and discharging system fault diagnosis method based on multi-cell spatial filtering provided in Example 1 of the present invention.

[0029] Figure 2 Schematic diagram of the circuit structure of the Buck-Boost converter system provided in Example 1 of the present invention.

[0030] Figure 3A The state variable obtained by using the present embodiment 1 and the existing method when there is no system failure is provided in the embodiment 1 of the present invention. Estimation curves and state variables Simulation comparison chart of the estimated curve; Figure 3B The state variable obtained by using the present embodiment 1 and the existing method when a system failure occurs is provided in the embodiment 1 of the present invention. Estimation curves and state variables Simulation comparison chart of the estimated curve; Figure 3C The state variable obtained by using the present embodiment 1 and the existing method when a system failure occurs is provided in the embodiment 1 of the present invention. and state variables Simulation comparison chart of the estimated curve; Figure 3D The method is provided in Example 1 of the present invention by using Example 1 and the existing method to obtain k =50, k =100, k =150, k =200 when the estimated range is obtained. DETAILED DESCRIPTION

[0031] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0032] Embodiment 1: This embodiment takes the Buck-Boost converter system as an example. Figure 1 and Figure 2 The fault diagnosis method of the battery charging and discharging system based on multi-cell spatial filtering is described. The Buck-Boost converter system is a power electronic device used for voltage step-up and step-down conversion, which is widely used in DC power supply systems. It mainly includes an input power supply, a switching element, a diode, an energy storage inductor, a capacitor and a load. During operation, the input power supply controls the transfer of energy through the switching element, accumulates energy in the energy storage inductor, rectifies through the diode and filters through the capacitor, and finally provides a stable output voltage. This embodiment is based on the capacitor voltage during the operation of the Buck-Boost converter system. and the inductor current The current value of the next moment is used to estimate the value of the next moment and determine whether the current system has a fault. Specifically, it includes: S100: For the battery charging and discharging system, a discrete state space model is constructed that takes into account the time delay and fault of the battery charging and discharging system; Specifically, step S100 includes: S110. A mathematical model is established for the Buck-Boost converter system, as shown in formula (1): (1) in, represents the inductor current, represents the capacitor voltage, Indicates voltage, Indicates the load of the circuit, Represents capacitance, Indicates the inductance value, Indicates the duty cycle of the transistor PWM signal; S120, discretize the mathematical model of the Buck-Boost converter system represented by formula (1), as shown in formula (2):

[0033] in, , represents a natural constant, represents the sampling period of the discrete system; S130. According to the mathematical model of the Buck-Boost converter system, a corresponding discrete state space model considering the battery charging and discharging system time lag and battery charging and discharging system faults is constructed, as shown in formula (3): (3) in, express The status of the battery charging and discharging system at all times, express The status of the battery charging and discharging system at all times, , express The measurement output at the moment, , represents a known input, Indicates a fault in the battery charging and discharging system. , , A , , B , C , D , E and F Both represent coefficient matrices of given preset dimensions, h represents the known constant time lag, , , , , , , , , represents the unknown but bounded process noise, represents the unknown but bounded measurement noise, , , represents the set of real numbers, represents the set of positive integers, express A column vector of real elements, express A column vector of real elements, express A column vector of real elements, express A column vector of real elements, express A column vector of real elements, express A column vector of real elements, The dimension is The matrix of The dimension is The matrix of The dimension is The matrix of The dimension is The matrix of The dimension is The matrix of .

[0034] It needs to be explained that The measured output represented is the system result that can be directly observed or measured. Specifically, it refers to the inductor current that can be directly measured when the Buck-Boost converter system is running. and the voltage across the capacitor .

[0035] Process noise and measurement noise The following formula (4) must be met: (4) in, and Both represent a unit box with preset dimensions. represents the process noise polytope generating matrix, represents the measurement noise polytope generator matrix.

[0036] S200, performing state augmentation on the discrete state space model to obtain a discrete state space reconstruction model; Specifically, the embodiment of the present invention will system failure Incorporate the discrete state space model of the battery charging and discharging system as part of the state estimation, and then use the augmented state vector , Simplify the discrete state space reconstruction model, including: S210, introducing augmented state vector , rewrite the above discrete state space model to obtain the discrete state space intermediate model, as shown in the following formula (5): (5) in, , , , , , , , express A column vector of real elements; S220, Introducing the Augmented State Vector Based on the Discrete State Space Intermediate Model , rewrite the discrete state space intermediate model to obtain the discrete state space reconstruction model, as shown in formula (6): (6) in, , , , , , , express A column vector of real elements, Represents the identity matrix.

[0037] By introducing Performance indicators, design robustness-enhanced observers to suppress uncertainty interference, and combine state augmentation technology to effectively decouple time-delay and fault signals, while simplifying the observer design process.

[0038] S300, for the discrete state space reconstruction model, build Observer, and introduce Lyapunov (Lyapunov) function and Performance indicators, calculation The coefficient matrix of the observer; Specifically, step S300 includes: S310, Build Observer, The observer is shown in the following formula (7): (7) in, express The augmented state estimate at time , express The measurement output at the moment, S , J , L Both represent coefficient matrices, , , , , , , P represents the positive definite matrix in the Lyapunov function, W , Y All represent the coefficient matrices to be determined, λ , μ Both represent coefficient matrices, , , , , The dimension is The matrix of The dimension is The matrix of .

[0039] S320, Introduction Lyapunov Functions and Observer Performance Indicators J Find S , J , L Optimal coefficient matrix: First, introduce the residual parameter , we can get the following formula (8): (8) Next, introduce Lyapunov function , we can get the following formula (9): (9) Then select the following formula (10) as Performance indicators of the observer: (10) in, γ represents the scalar coefficient that measures the degree of polytope shrinkage; according to is always less than 0, and formula (11) can be derived: (11) According to formula (7) Observer, substitute formula (9) into formula (11) to construct the LMI inequality, so that the current k Time Polytopic Space Residual The gain problem of minimizing the state estimation error is transformed into the problem of LMI optimal solution. The LMI inequality is shown in formula (12): (12) in, , , , , M Represents the augmented state vector The matrix of system state estimates extracted from express dimensional identity matrix, express dimensional identity matrix, express dimensional identity matrix.

[0040] Obtain S , J , L The optimal coefficient matrix is ​​shown in formula (13): (13) in, , .

[0041] S400, yes The state estimation value of the battery charging and discharging system output by the observer is expressed in a multi-cellular space; That is Augmented state estimation of battery charging and discharging system based on observer output Perform multi-cell space expression, that is, give the current k Time to k The iterative form of the polyhedral space at time +1; Specifically, given Time system augmentation status , then there exists a coefficient matrix , , ,make The augmented state estimate of the system at time satisfies the following formula (14): (14) in, , , Indicates package The coordinate vector of the center point of the polyhedron of the system state at the moment, Indicates package The shape matrix of the polyhedron of the system state at the moment, Indicates package The coordinate vector of the center point of the polyhedron of the system state at the moment, Indicates package The shape matrix of the polyhedron of the system state at the moment, represents the polytope generating matrix that encapsulates the measurement noise of the system, Represents the polytope generating matrix that encapsulates the process noise of the system.

[0042] It should be understood that in formula (7) It is a recursive formula of discrete system. If the polyhedral space is used as the state variable of the previous moment into formula (7), the polyhedral space range of the state estimation at the next moment can be obtained, as shown in formula (14) here, and specifically as shown in the following formula (15):

[0043] S500, according to the multicellular space expression The observer, based on the polyhedral space Kalman filter, constructs the target by observing the residual in real time Observer, Target The boundary of the observer's state estimate polyhedral space is less than The state of the observer estimates the polyhedral space boundary, the target The output of the observer is the target augmented state estimate of the battery charging and discharging system; Specifically, the goal The observer is designed as an optimized estimate of the augmented state vector, and its structure is shown in formula (16):

[0044] in, , , , represents the covariance in Kalman filtering, , and All represent intermediate parameters; Given Constantly expanding status When Time target The target augmented state estimate output by the observer is as follows: (17) in, , Indicates package The target center point coordinate vector of the polyhedron of the system state at the moment, Indicates package The target shape matrix of the polyhedron of the system state at time instant, I represents the identity matrix, express Augment the state vector at any moment Estimated polytopic space.

[0045] The embodiment of the present invention describes the state space by using a polyhedral space Kalman filter, and Frobenius (Frobenius) radius minimization criterion to construct more accurate models from real-time observation residuals The observer achieves a balance between the contraction of the estimation interval and the computational complexity, further narrows the polyhedral space boundary of the state estimation, and can greatly improve the accuracy of the system state estimation.

[0046] S600, separating the system state estimate and the system fault estimate from the target augmented state estimate, calculating the envelope interval of the system fault estimate, and obtaining the upper and lower bounds of the envelope interval of the system fault estimate; Specifically, k The target augmented state estimate at time +1 is ,but kState estimation at time +1 As shown in formula (18): (18) in, Represents the separation based on the target augmented state estimate The system state estimate at time M Represents the augmented state vector The matrix of system state estimates extracted from , , express dimensional identity matrix, N Represents the augmented state vector The matrix of system fault estimates extracted from , , express dimensional identity matrix, R Represents the augmented state vector The augmented state vector extracted from The matrix of , express dimensional identity matrix; The system fault estimation is as follows: (19) in, Represents the separation based on the target augmented state estimate k System failure estimation at the moment; For any polytope , there is a minimum interval envelope ,make , from which the expression of the envelope interval of system fault estimation is obtained as formula (20): (20) in, represents the upper bound of the envelope interval of system fault estimation, represents the lower bound of the envelope interval of system fault estimation, , , Represents a polyhedral space Medium shape generation matrix No. All elements of the row Frobenius The sum of norms.

[0047] S700. Analyze the occurrence of faults in the battery charge and discharge system based on the upper and lower bounds of the envelope interval estimated according to system faults, and obtain the fault diagnosis results of the battery charge and discharge system.

[0048] The fault judgment criteria for the time-delay battery charge and discharge system with system faults are as follows: If there exists , it means ; otherwise, . When , it means that no system fault occurs at time , and at this time . On the contrary, when , it means that a system fault occurs, that is .

[0049] The fault diagnosis method for the battery charge and discharge system based on zonotope space filtering provided by the embodiments of the present invention breaks through the limitations of traditional single-stage filters in time-delay systems and fault detection by constructing a triple collaborative filtering structure including state augmentation, ZKF optimization, and dynamic residual generator, and innovatively realizes the comprehensive optimization of state estimation accuracy, fault detection sensitivity, and system robustness. As the core, ZKF describes the state space using the Zonotope set and realizes the balance between the contraction of the estimation interval and the computational complexity through the Frobenius radius minimization criterion. Further introduce the H performance index, design an observer with enhanced robustness to suppress uncertainty interference, and effectively decouple the time-delay and fault signals by combining the state augmentation technology, while simplifying the observer design process. To improve the fault detection ability, a detection module based on a dynamic residual generator is also constructed, and high sensitivity and low missed detection rate are achieved by combining the geometric characteristics of the Zonotope set. Furthermore, the balance problem between robustness and accuracy of traditional filtering methods is optimized, providing an efficient solution for state estimation and fault detection of complex time-delay systems.

[0050] To verify the effectiveness of the fault diagnosis method for the battery charge and discharge system based on zonotope space filtering provided by this embodiment, this method is compared with the existing method, and the experimental simulation results are as shown in Figure 3A , Figure 3B and Figure 3C , where Figure 3A is the estimated curve of the state variable and the estimated curve of the state variable of the Buck-Boost converter system when the system has no faults, Figure 3B is the estimated curve of the state variable and the estimated curve of the state variable of the Buck-Boost converter system when the system has faults, Figure 3Cis the state variable of the Buck-Boost converter system when a fault occurs in the system and Estimation curve. Figure 3A , Figure 3B and Figure 3C In the figure, the solid line represents the true value, the thick dashed line represents the estimated upper and lower bounds obtained by the method proposed in the present invention, and the dotted line and the thin dashed line represent the estimated upper and lower bounds of other existing methods. It can be seen that the upper and lower bounds of each method can contain the true state of the Buck-Boost converter system when the system fails, but the TS-ZKF H in the battery charging and discharging system fault diagnosis method based on polycellular spatial filtering provided in this embodiment is The state estimation interval ratio interval set inversion observer (MCIO H observer) and zonotope H observer) is narrower, indicating that the battery charging and discharging system fault diagnosis method based on polyhedral space filtering of this embodiment can achieve more accurate fault estimation, and the effect is smooth and close to the true value; at the same time, the boundary of the fault estimation will change with the occurrence of the fault, which is further made more accurate and can detect the fault earlier and more accurately.

[0051] Figure 3D Estimation range curves of the method provided in this embodiment and the existing method at different times (k = 50, k = 100, k = 150, k = 200) are respectively given. Figure 3D middle, The dot represents the true value at the current moment, the thick dashed line represents the estimated range obtained by the method provided in this embodiment, and the dotted line and the thin dashed line represent the existing method zonotope H It can be seen that the estimated range of the method proposed in this invention at each time step is significantly close to the true value, and the state estimation range at different time points is more compact; when k=100, only TS-ZKF H Observer and zonotope H The estimated interval of the observer can detect the occurrence of faults, while the MCIO algorithm has a certain false alarm rate. This is because TS-ZKF H The observer's estimated range is larger than that of TS-ZKF H Observer and MICO have small estimation ranges and are therefore more sensitive in fault detection.

[0052] Embodiment 2: The embodiment of the present invention provides a battery charging and discharging system fault diagnosis device based on polycellular spatial filtering, which is used to implement the above-mentioned battery charging and discharging system fault diagnosis method based on polycellular spatial filtering, including: A model building module is used to build a discrete state space model for a battery charging and discharging system that takes into account the time delay and faults of the battery charging and discharging system; A model reconstruction module is used to perform state augmentation on the discrete state space model to obtain a discrete state space reconstruction model; The observer building module is used to reconstruct the model for the discrete state space and build Observer, and introduce Lyapunov Functions and Performance indicators, calculation The coefficient matrix of the observer; Multicellular spatial expression module for The augmented state estimate of the battery charging and discharging system output by the observer is expressed in a polyhedral space; Target The observer building block is used to represent the polyhedral space The observer, based on the polyhedral space Kalman filter, constructs the target by observing the residual in real time Observer, Target The boundary of the observer's state estimate polyhedral space is less than The state of the observer estimates the polyhedral space boundary, the target The output of the observer is the target augmented state estimate of the battery charging and discharging system; An envelope interval calculation module is used to separate the system state estimate and the system fault estimate from the augmented state estimate, calculate the envelope interval of the system fault estimate, and obtain the upper and lower bounds of the envelope interval of the system fault estimate; The judgment module is used to analyze the occurrence of the battery charging and discharging system faults according to the upper and lower bounds of the envelope interval estimated by the system fault, and obtain the fault diagnosis result of the battery charging and discharging system.

[0053] The battery charging and discharging system fault diagnosis device based on polyhedral space filtering provided by the embodiment of the present invention breaks through the limitations of traditional single-stage filters in time-delay systems and fault detection by constructing a triple collaborative filtering structure including state augmentation, ZKF optimization and dynamic residual generation, and innovatively realizes the comprehensive optimization of state estimation accuracy, fault detection sensitivity and system robustness. The core ZKF uses the Zonotope set to describe the state space, and achieves a balance between the contraction of the estimation interval and the computational complexity through the Frobenius radius minimization criterion. Further introducing H Performance indicators, design robustness-enhanced observers to suppress uncertainty interference, and combine state augmentation technology to effectively decouple time-delay and fault signals, while simplifying the observer design process. In order to improve the fault detection capability, a detection module based on a dynamic residual generator is also constructed, which combines the geometric characteristics of the Zonotope set to achieve high sensitivity and low missed detection rate. This optimizes the balance between robustness and accuracy of traditional filtering methods, and provides an efficient solution for state estimation and fault detection of complex time-delay systems.

[0054] The specific principle of the battery charging and discharging system fault diagnosis device based on multi-cell spatial filtering provided in the embodiment of the present invention can refer to the principle description of the battery charging and discharging system fault diagnosis method based on multi-cell spatial filtering, and the embodiment of the present invention will not be described in detail here.

[0055] Embodiment 3: An embodiment of the present invention provides a battery charging and discharging system, including: The battery charging and discharging system body is used to control the battery charging and discharging process, manage and adjust the battery power, and make the battery work within the predetermined charging and discharging range; The battery charging and discharging system fault diagnosis device based on polyhedral spatial filtering is used to perform fault diagnosis on the battery charging and discharging system; The DC-DC circuit system is used to convert the input voltage to a predetermined battery charge and discharge voltage range.

[0056] The embodiment of the present invention breaks through the limitations of traditional single-stage filters in time-delay systems and fault detection through a battery charging and discharging system fault diagnosis device based on polyhedral space filtering, and can achieve comprehensive optimization of state estimation accuracy, fault detection sensitivity and system robustness when performing state estimation and fault detection on the battery charging and discharging system body.

[0057] The specific principle of the battery charging and discharging system provided by the embodiment of the present invention can refer to the principle description of the battery charging and discharging system fault diagnosis method based on multi-cell space filtering, and the embodiment of the present invention will not be described in detail here.

[0058] Some steps in the embodiments of the present invention may be implemented using software, and the corresponding software program may be stored in a readable storage medium, such as a CD or a hard disk.

[0059] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A battery charging and discharging system fault diagnosis method based on multi-cell spatial filtering, characterized in that: include: For the battery charging and discharging system, a discrete state space model is constructed that takes into account the time delay and faults of the battery charging and discharging system. Performing state augmentation on the discrete state space model to obtain a discrete state space reconstruction model; For the discrete state space reconstruction model, construct Observer, and introduce Lyapunov Functions and Performance indicators, calculated as described The coefficient matrix of the observer; Regarding the The augmented state estimate of the battery charging and discharging system output by the observer is expressed in a polyhedral space; According to the description after multicellular spatial expression The observer, based on the polyhedral space Kalman filter, constructs the target by observing the residual in real time Observer, the target The boundary of the observer's state estimate polyhedral space is smaller than the The state of the observer is estimated by the polyhedral space boundary, the target The output of the observer is the target augmented state estimate of the battery charging and discharging system; Separating a system state estimate and a system fault estimate from the target augmented state estimate, and calculating an envelope interval of the system fault estimate to obtain upper and lower bounds of the envelope interval of the system fault estimate; According to the upper and lower bounds of the envelope interval of the system fault estimation, the fault occurrence of the battery charging and discharging system is analyzed to obtain the fault diagnosis result of the battery charging and discharging system.

2. The method according to claim 1, characterized in that The expression of the discrete state space model is: in, express The status of the battery charging and discharging system at all times, express The status of the battery charging and discharging system at all times, , express The measurement output at the moment, , represents a known input, Indicates a fault in the battery charging and discharging system. , , A , , B , C , D , E and F Both represent coefficient matrices of given preset dimensions, h represents the known constant time lag, , , , , , , , , represents the unknown but bounded process noise, represents the unknown but bounded measurement noise, , , represents the set of real numbers, represents the set of positive integers, express A column vector of real elements, express A column vector of real elements, express A column vector of real elements, express A column vector of real elements, express A column vector of real elements, express A column vector of real elements, The dimension is The matrix of The dimension is The matrix of The dimension is The matrix of The dimension is The matrix of The dimension is The matrix of .

3. The method according to claim 2, characterized in that The step of performing state augmentation on the discrete state space model to obtain a discrete state space reconstruction model includes: Introducing the augmented state vector , rewrite the above discrete state space model to obtain a discrete state space intermediate model, the expression of the discrete state space intermediate model is: in, , , express A column vector of real elements; Introducing the augmented state vector , rewrite the discrete state space intermediate model to obtain the discrete state space reconstruction model, the expression of the discrete state space reconstruction model is: in, , express A column vector of real elements, Represents the identity matrix.

4. The method according to claim 3, characterized in that Said The expression of the observer is: in, express The augmented state estimate at time , express The measurement output at the moment, S , J , L Both represent coefficient matrices, P represents the positive definite matrix in the Lyapunov function, W , Y All represent the coefficient matrices to be determined, λ , μ Both represent coefficient matrices, The dimension is The matrix of The dimension is The matrix of .

5. The method according to claim 4, characterized in that Said to Said The state estimation value of the battery charging and discharging system output by the observer is expressed in a polyhedral space, including: According to the given System status at all times and the coefficient matrix S , J , L ,get The expression of the augmented state estimate of the system state at time is: in, , , Indicates package The coordinate vector of the center point of the polyhedron of the system state at the moment, Indicates package The shape matrix of the polyhedron of the system state at the moment, Indicates package The coordinate vector of the center point of the polyhedron of the system state at the moment, Indicates package The shape matrix of the polyhedron of the system state at the moment, represents the polytope generating matrix that encapsulates the measurement noise of the system, Represents the polytope generating matrix that encapsulates the process noise of the system.

6. The method according to claim 5, characterized in that The stated goal The expression of the observer is: in, , , , represents the covariance in Kalman filtering, , and All represent intermediate parameters; In a given +1 time system status is hour, Time target The expression of the target augmented state estimate output by the observer is: in, , Indicates package The target center point coordinate vector of the polyhedron of the system state at the moment, Indicates package The target shape matrix of the polyhedron of the system state at time instant, I represents the identity matrix, express Augment the state vector at any moment Estimated polytopic space.

7. The method according to claim 6, characterized in that The expression of the system state estimation is: in, Represents the separation based on the target augmented state estimate The system state estimate at time M Represents the augmented state vector The matrix of system state estimates extracted from , , express dimensional identity matrix, N Represents the augmented state vector The matrix of system fault estimates extracted from , , express dimensional identity matrix, R Represents the augmented state vector The augmented state vector extracted from The matrix of , express dimensional identity matrix; The expression of the system fault estimation is: in, Represents the separation based on the target augmented state estimate k System failure estimation at the moment; The expression of the envelope interval of the system fault estimation is: in, represents the upper bound of the envelope interval of system fault estimation, represents the lower bound of the envelope interval of system fault estimation, , , Represents a polyhedral space Medium shape generation matrix No. All elements of the row Frobenius The sum of norms.

8. The method according to claim 7, characterized in that The method of analyzing the occurrence of a fault in the battery charging and discharging system according to the upper and lower bounds of the envelope interval estimated by the system fault, and obtaining a fault diagnosis result of the battery charging and discharging system, comprises: if , make sure that there is no fault in the battery charging and discharging system; if or , determine that the battery charging and discharging system is faulty.

9. A battery charging and discharging system fault diagnosis device based on polycellular spatial filtering, used to implement the battery charging and discharging system fault diagnosis method based on polycellular spatial filtering according to any one of claims 1 to 8, characterized in that: include: A model building module is used to build a discrete state space model for a battery charging and discharging system that takes into account the time delay and faults of the battery charging and discharging system; A model reconstruction module, used for performing state augmentation on the discrete state space model to obtain a discrete state space reconstruction model; An observer construction module is used to reconstruct the model of the discrete state space and construct Observer, and introduce Lyapunov Functions and Performance indicators, calculated as described The coefficient matrix of the observer; Multicellular spatial expression module, used to The augmented state estimate of the battery charging and discharging system output by the observer is expressed in a polyhedral space; Target The observer building module is used to express the polyhedral space according to the The observer, based on the polyhedral space Kalman filter, constructs the target by observing the residual in real time Observer, the target The boundary of the observer's state estimate polyhedral space is smaller than the The state of the observer is estimated by the polyhedral space boundary, the target The output of the observer is the target augmented state estimate of the battery charging and discharging system; An envelope interval calculation module, used to separate the system state estimate and the system fault estimate from the augmented state estimate, and calculate the envelope interval of the system fault estimate to obtain the upper and lower bounds of the envelope interval of the system fault estimate; The judgment module is used to analyze the occurrence of the battery charging and discharging system fault according to the upper and lower bounds of the envelope interval of the system fault estimation, and obtain the fault diagnosis result of the battery charging and discharging system.

10. A battery charging and discharging system, characterized in that: include: The battery charging and discharging system body is used to control the battery charging and discharging process, manage and adjust the battery power, and make the battery work within the predetermined charging and discharging range; The battery charging and discharging system fault diagnosis device based on multi-cell spatial filtering as described in claim 9 is used to perform fault diagnosis on the battery charging and discharging system; The DC-DC circuit system is used to convert the input voltage to a predetermined battery charge and discharge voltage range.

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