Active isolation method for fault of traction battery formation device

Abstract

The present invention belongs to the technical field of traction battery fault diagnosis. Disclosed are an active isolation method for a fault of a traction battery formation device. In the method, a traction battery formation device state model including an unknown-but-bounded noise and fault is acquired, a space update strategy is designed on the basis of a fully symmetric polytopic set-membership filtering concept, a state observer is constructed, and in this case, it is not necessary to assume that the prior distribution of the noise of the model is known, such that the robustness and practicability of an algorithm are enhanced; a test point is introduced to execute fault detection, the tangency between a state admissible set and a fault set is denoted as a critical condition for the occurrence of the fault, and the critical condition is described as two optimization problems to directly solve the values of auxiliary signals, such that effective isolation for the fault is realized, thereby avoiding the problem complexity caused by auxiliary signals being designed on the basis of performance indicators of an observer in a conventional method; and compared with the conventional method, the method has a higher sensitivity to a tiny fault and can thus avoid delayed detection or missed detection for the fault, thereby improving the efficiency of fault detection and isolation.

Description

A method for actively isolating power battery formation equipment faults

[0001] An Active Fault Isolation Method for Power Battery Formation Equipment Technical Field

[0002] The invention relates to a method for actively isolating power battery formation equipment fault, and belongs to the technical field of power battery fault diagnosis. Background Art

[0003] In recent years, driven by the rapid expansion of the new energy vehicle market and other factors, demand for high-end power battery products has shown a significant upward trend in a wide range of fields, including scientific research, industrial production, and daily life. Battery performance is directly related to the operating efficiency and service life of equipment, making battery quality control a critical step in the production process. The formation process plays a vital role in power battery production. This step activates the lithium-ion batteries, ensuring consistent performance during assembly and becoming a key factor influencing power battery quality control. The technical level and operational precision of this process are directly related to the reliability, safety, and service life of the battery. Therefore, by implementing proactive fault detection and isolation measures, the technical level and process quality of the formation process can be improved, promoting the sustainable and healthy development of the new energy vehicle industry.

[0004] The battery formation process involves repeatedly charging and discharging batteries using a formation power converter during the battery manufacturing process to activate their internal chemical activity. Active fault isolation using auxiliary signals is a widely used fault diagnosis method. It leverages the differences between system inputs and outputs to proactively isolate and identify faults by adding specific auxiliary signals.

[0005] Traditional auxiliary signal design methods, such as those based on frequency domain analysis and models, can be sensitive to noise and changes in system parameters when designing auxiliary signals. This can lead to insufficient robustness in practical applications. This means that after a fault is identified, it cannot be effectively isolated in real time due to changes in system parameters. Furthermore, traditional auxiliary signal design methods often use observer performance metrics as the standard for auxiliary signal design, which not only increases the complexity of the problem but also may reduce the efficiency of fault detection. In the early stages of a fault, the system performance may only decrease slightly, which may cause the auxiliary signal design method to fail to detect the fault, or detect it only after the fault has developed to a certain extent, thereby delaying fault diagnosis. Summary of the Invention

[0006] In order to solve the above problems, the present invention provides a method for actively isolating faults in power battery formation equipment, which is implemented based on polyhedral filtering and auxiliary signal design. The method includes:

[0007] Step S1: Establishing a power battery formation equipment model including unknown but bounded noise and faults;

[0008] First, based on the similar functions and behaviors of the battery formation power conversion device and the Buck circuit in the electrical circuit, the power battery formation device is simplified into a buck converter circuit analysis, and the corresponding buck converter circuit model is obtained as follows:

[0009] Among them, R load , L, C, and D are the load resistance, inductance, capacitance, and duty cycle parameters of the buck converter circuit respectively; select the capacitor voltage U c (t) and the inductor current I L (t) is the state of the system, so x(t) = [x1(t)x2(t)] T =[I L (t)U c (t)] T ; Input voltage E(t) and load current I load (t) is the external input variable of the system, and u(t)=[E(t)I load (t)] T .

[0010] After obtaining the corresponding buck converter circuit model, it is discretized to obtain the power battery formation device state model that includes unknown but bounded noise and faults:

[0011] in, Represents the state of the i-th model at time k, with the capacitor voltage U in the buck converter circuit c (t) and the inductor current I L (t) as the state of the model; represents the input of the i-th model at time k, i.e., the charge / discharge current and / or charge / discharge voltage; represents the output of the i-th model at time k, which is the load battery voltage or load battery capacity;

[0012] A [i] Represents the known state transfer matrix, B [i] Represents the known input matrix, C [i] represents the known output matrix;

[0013] represents the unknown but bounded disturbance noise at time k; represents the unknown but bounded measurement noise at time k; h k Indicates the auxiliary input signal.

[0014] Step S2: Design a fully symmetric polyhedron observer to estimate the state at the next moment k based on the state at the current moment k-1, thereby obtaining a fully symmetric polyhedron expression that encapsulates the true state at the moment k.

[0015] i∈{0,...,ξ} is the corresponding model index, ξ represents the fault sequence number; when i=0, the model is in a normal state, indicating that the power battery formation equipment is operating normally; otherwise, the model is in a fault state, indicating that the power battery formation equipment has a fault;

[0016] Step S3: Obtaining a measurement state set according to the power battery formation equipment model Expressions of

[0017] Step S4: Based on the real state at time k Located in a fully symmetric polyhedron and measurement state set The auxiliary signal h used to separate the normal model and the fault model is k The design problem is transformed into the following optimization problem:

[0018] represents the optimal polytopic outer bound containing the system state at time k in the normal model, represents the optimal polytopic external bound containing the system state at time k in the fault model;

[0019] Step S5: Find the normal model by adding test points and fault models The equivalent condition of the intersection being empty forms two different optimization problems. Solving the two optimization problems separately will yield two different auxiliary signals h1 and h2.

[0020] Usually, the state point that makes the polyhedron representing the normal model and the polyhedron representing the fault model have only one intersection is called the state separation point. A series of state points with the same mathematical form and undetermined coefficients as the state separation point are called test points. This application finds the normal model by adding test points. and fault models Equivalence condition for empty intersection.

[0021] Step S6: Select one of the auxiliary signals h1 and h2 as the final auxiliary signal h kThe battery-to-power conversion equipment state model is introduced to complete the active isolation of faults that occur when the equipment is in operation.

[0022] Optionally, in step S2, the expression for estimating the state at the next time k based on the state at the current time k-1 is:

[0023] in, That is, corresponding to the i-th model, the state at the next moment k is estimated based on the state at the current moment k-1; It indicates that the center of the fully symmetric polyhedron predicted at time k by the i-th model based on the prediction at time k-1, represents the matrix generated by this polyhedron; Is a combination of r+n x unit box composed of unit intervals; r corresponds to the dimension of the fully symmetric polyhedron, n x corresponds to the dimension of the perturbation noise.

[0024] Optionally, step S3 includes:

[0025] According to the power battery formation equipment state model shown in formula (1), the output of the i-th model at time k is derived as follows: Then we get the measurement state set The expression:

[0026] Wherein, σ represents the relative coefficient of the center interval of the measurement noise relative to the unit interval. Optionally, in step S4, the real state at time k Located in a fully symmetric polyhedron With measuring tape Inside the intersection, the optimal polytope outside the intersection is bounded The center and generator matrix are:

[0027] in

[0028] I represents the identity matrix and T represents the transpose operation.

[0029] Optionally, step S5 includes:

[0030] For λ1,λ2∈[0,1] that satisfy the following equations (10) and (11):

[0031] like:

[0032] or

[0033] Comprehensively consider equations (12), (13) and λ1, λ2, The prior condition of , the optimization problem described by formula (9) is comprehensively expressed as:

[0034] or

[0035] Solve the two different optimization problems in equation (14) and equation (15) respectively, and obtain two different auxiliary signals h1 and h2.

[0036] Optionally, step S6 includes:

[0037] The auxiliary signal h is finally selected according to the following rules k :

[0038] h k =min{|h1|,|h2|} (16)

[0039] The auxiliary signal h k The battery-to-power conversion equipment state model is introduced to complete the active isolation of faults that occur when the equipment is in operation.

[0040] Optionally, select the output matrix C [i] When

[0001] , the output variable y(t) = U c (t).

[0041] The present application also provides a method for monitoring the operation of a power battery formation equipment, which uses the above method to actively isolate faults that occur during the operation of the power battery formation equipment.

[0042] The beneficial effects of the present invention are:

[0043] Different from the traditional auxiliary signal design method, by obtaining the state model of the power battery formation equipment containing unknown but bounded noise and faults, based on the idea of ​​fully symmetric polyhedron set membership filtering, a spatial update strategy is designed to obtain the optimal polyhedron external delimitation of the state quantity by taking the intersection of the prediction step fully symmetric polyhedron and the measurement band, and a state observer is constructed. At this time, there is no need to assume that the prior distribution of the model noise is known, which enhances the robustness and practicality of the algorithm; by introducing test points to perform fault detection, the tangency of the state feasible set and the fault set is expressed as the critical condition for the occurrence of the fault, and this critical condition is described as two optimization problems to directly obtain the auxiliary signal value, thereby realizing effective fault isolation and avoiding the complexity of the problem caused by relying on the observer performance indicators to design auxiliary signals in the traditional method; simulation examples have proved that compared with the traditional method, the method provided by the present application is more sensitive to minor faults, can better avoid delayed or missed detection of faults, and improves the efficiency of fault detection and isolation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] 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.

[0045] FIG1 is a flow chart of a method for actively isolating faults in power battery formation equipment based on multi-cell filtering and auxiliary signal design disclosed in one embodiment of the present application.

[0046] FIG2 is an equivalent circuit model diagram of a power battery formation device (simplified as a buck circuit for analysis).

[0047] FIG3A is a simulation diagram of system state estimation and fault isolation results at time k=100 when no auxiliary signal is added when a power battery fails, as disclosed in one embodiment of the present application;

[0048] FIG3B is a simulation diagram of system state estimation and fault isolation results at time k=200 when no auxiliary signal is added when a power battery fails, as disclosed in one embodiment of the present application;

[0049] FIG3C is a simulation diagram of system state estimation and fault isolation results at time k=300 in a case where no auxiliary signal is added when a power battery fails, as disclosed in one embodiment of the present application;

[0050] FIG3D is a simulation diagram of system state estimation and fault isolation results at time k=400 in a case where no auxiliary signal is added when a power battery fails, as disclosed in one embodiment of the present application.

[0051] FIG4A is a diagram showing system state estimation and fault isolation results at time k=100 when an auxiliary signal is added using an existing method when a power battery fails, as disclosed in one embodiment of the present application;

[0052] FIG4B is a diagram showing system state estimation and fault isolation results at time k=200 when an auxiliary signal is added using an existing method when a power battery fails according to an embodiment of the present application;

[0053] FIG4C is a diagram showing system state estimation and fault isolation results at time k=300 when an auxiliary signal is added using an existing method when a power battery fails according to an embodiment of the present application;

[0054] FIG4D is a diagram showing system state estimation and fault isolation results at time k=400 when an auxiliary signal is added using an existing method when a power battery fails, as disclosed in one embodiment of the present application.

[0055] FIG5A is a diagram showing system state estimation and fault isolation results at time k=100 when an auxiliary signal is added using the method provided in the present application when a power battery failure occurs in one embodiment of the present application;

[0056] FIG5B is a diagram showing system state estimation and fault isolation results at time k=200 when an auxiliary signal is added using the method provided in the present application when a power battery failure occurs in one embodiment of the present application;

[0057] FIG5C is a diagram showing system state estimation and fault isolation results at time k=300 when an auxiliary signal is added using the method provided in the present application when a power battery failure occurs in one embodiment of the present application;

[0058] FIG5D is a diagram showing the system state estimation and fault isolation results at time k=400 when an auxiliary signal is added using the method provided in the present application when a power battery failure occurs in an embodiment disclosed in the present application. DETAILED DESCRIPTION

[0059] To make the purpose, technical solutions and advantages of the present invention more clear, the following will further describe the embodiments of the present invention in detail with reference to the accompanying drawings. First, the basic theory involved in this application is introduced as follows:

[0060] (1) This application simplifies the battery formation device into a Buck circuit for analysis. This is because the battery formation power conversion device and the Buck circuit have similar functions and behaviors in the electrical circuit, which are specifically manifested in the following five aspects:

[0061] 1. Voltage conversion: During the battery formation process, a higher DC voltage is typically converted to the lower voltage required for battery charging. A buck circuit is a step-down converter that converts a higher DC input voltage into a lower, stable output voltage. This aligns with the voltage conversion requirements during battery formation.

[0062] 2. Energy transfer: During the battery formation process, energy needs to be transferred from the grid to the battery, which involves energy transfer and conversion. The Buck circuit transfers and converts energy through its switching elements and energy storage elements (inductors and capacitors), allowing the input energy to be efficiently transferred to the load (in the case of battery formation, the load is the battery).

[0063] 3. Control interface: To achieve accurate battery charging, a control system is required to regulate the charging current and voltage. Buck circuits are usually equipped with a control system that can control the output voltage and current by adjusting the switching frequency and duty cycle, which provides a controllable interface for battery formation.

[0064] 4. Stability: The output voltage and current need to be stable during the battery formation process to ensure battery safety and performance. The Buck circuit is designed to provide a stable output, even when the input voltage and load conditions change.

[0065] 5. Efficiency: Efficient energy conversion is very important for battery-based devices because it is related to energy utilization and cost; the Buck circuit is known for its high efficiency and can transfer energy from input to output with minimal loss.

[0066] Therefore, when conducting theoretical analysis, the battery charging device can be simplified into a Buck circuit.

[0067] (2) In the prior art, fault detection based on set membership estimation is generally performed by checking whether the approximate feasible set is an empty set. Its fault detection strategy can be expressed as follows: if the approximate feasible set is detected as an empty set, the system is considered to have a fault; otherwise, the system is considered to have no fault. For normal and fault models (i.e., normal state and fault state), the fault isolation process involves checking whether the intersection of the two strips is empty. If the intersection is empty, it indicates that the system is normal. Otherwise, it cannot be determined that the system is normal, that is, the system has a fault.

[0068] (3) The relevant concepts and symbols of fully symmetric polytope set membership filtering are introduced as follows:

[0069] X=[a,b] represents an interval set {x∈X:a≤x≤b}, B=[-1,1] represents a unit interval, B r is a unit box consisting of r unit intervals; the Minkowski sum of two sets X and Y is defined as An expression for a fully symmetric polytope is of the form where p∈R n , G∈R n×r , r is the dimension of this fully symmetric polyhedron.

[0070] Based on the above basic theory, the technical solution of this application is described in detail below in combination with specific embodiments:

[0071] Example 1

[0072] This embodiment provides a method for actively isolating power battery formation equipment faults based on polycell filtering and auxiliary signal design. Referring to FIG1 , the method includes:

[0073] Step 1: Simplify the power battery formation equipment into a buck circuit analysis and obtain the following equation:

[0074] Please refer to the buck converter circuit model shown in Figure 2, where R load , L, C, D are the load resistance, inductance, capacitance and duty cycle parameter values ​​of the buck circuit respectively. Select the capacitor voltage U c (t) and the inductor current I L (t) is the state of the system, so x(t) = [x1(t) x2(t)] T =[I L (t) U c (t)] T ; Input voltage E(t) and load current I load (t) is the external input variable of the system, and u(t)=[E(t) I load (t)] T .

[0075] Step 2: After discretizing the buck circuit model shown in formula (17), the power battery formation device state model including unknown but bounded noise and faults shown in formula (1) is obtained:

[0076] Where i∈{0,…,ξ} is the corresponding model index, ξ represents the fault sequence number. When i=0, the model is in a normal state, otherwise it is in a fault state; the subscript k represents the discrete moment, represents the state of the i-th model at time k, Indicates n x dimensional real number space; Represents the input of the i-th model at time k, which is the charge and discharge current (also known as the inductor current I L (t)) or charge and discharge voltage (also known as capacitor voltage U c (t)), in this embodiment, it is a matrix composed of the two; A represents the output of the i-th model at time k, which is usually the load battery voltage or load battery capacity; [i] Represents the known state transfer matrix, B [i] Represents the known input matrix, C [i] represents the known output matrix; represents the unknown but bounded disturbance noise at time k, with an initial value W represents the generator matrix of the unknown but bounded perturbation noise; represents the unknown but bounded measurement noise at time k, the initial value V represents the generator matrix of the unknown but bounded measurement noise; h k Indicates the auxiliary input signal.

[0077] Step 3: Select the output matrix C [i] is

[0001] , then the output variable y(t) = U c (t).

[0078] Step 4: According to the current state of k-1 Estimate the state at the next moment

[0079] in, It indicates that the center of the fully symmetric polyhedron predicted at time k by the i-th model based on the prediction at time k-1, represents the generator matrix of this polyhedron; W is the generator matrix of the unknown but bounded perturbation noise.

[0080] Measurement noise In a central interval V = σB 1 Where (σ represents the relative coefficient of the center interval of the measurement noise relative to the unit interval), according to formula (1), the output measurement can be expressed as So the measurement state set It can be described as follows with a space:

[0081] The actual state at time k Located in a fully symmetric polyhedron With measuring tape Inside the intersection, the optimal polytope outside the intersection is bounded The center and generator matrix can be written as:

[0082] in

[0083] I represents the identity matrix and T represents the transpose operation.

[0084] Step 5: In order to separate the normal model and the fault model, the design of the auxiliary signal is transformed into the following optimization problem:

[0085] represents the optimal polytopic outer bound containing the system state at time k in the normal model, represents the optimal polytopic outer bound containing the system state at time k in the fault model.

[0086] To solve this optimization problem, this application finds The equivalent condition is that for λ1,λ2∈[0,1] that satisfies the following equations (10) and (11):

[0087] like:

[0088] or

[0089] Comprehensively consider equations (12), (13) and λ1, λ2, The prior condition of , the optimization problem described by formula (9) is comprehensively expressed as:

[0090] or

[0091] By solving the two different optimization problems in Equation (14) and Equation (15) respectively, two different auxiliary signals h1 and h2 can be obtained.

[0092] Step 6: Finally select the auxiliary signal h according to the following rules k : h k =min{|h1|,|h2|} (16)

[0093] The auxiliary signal h k Introduce the battery power conversion equipment state model.

[0094] In this embodiment, after executing steps 1 to 6 within a predetermined time range, the normal model and the fault model of the system are separated, completing the active isolation of the fault occurring in the equipment operation state.

[0095] To verify the effectiveness of the method of the present application, this embodiment conducts a simulation experiment on a power battery formation device (simplified as a buck circuit during analysis) using the method of the present application and the existing method. Table 1 shows the model parameters of the buck converter in the simulation experiment. The total number of iterations in the experimental process is k=400, and different numbers of iterations correspond to different moments.

[0096] Table 1 Buck converter model parameters

[0097] Figures 3A-3D, 4A-4D, and 5A-5D correspond to the system state estimation and fault isolation results of the power battery formation equipment (simplified as a buck circuit during analysis) at the same time without adding an auxiliary signal method, using the existing method (the existing method can be referred to as Active fault detection based on set-membership approach for uncertain discrete-time systems, International Journal of Robust and Nonlinear Control.), and using the method provided by this application, corresponding to different moments. The method of adding an auxiliary signal using the existing method is a method of obtaining an auxiliary signal by designing an observer. Different from the auxiliary signal obtained by solving the optimization problem in this application, the auxiliary signal of the existing method is obtained by designing an observer. When designing the observer, on the one hand, it relies on setting a certain threshold to determine whether the system state is abnormal. For minor faults, since their characteristics may not be enough to exceed this threshold, it is difficult to be detected; on the other hand, the accuracy limitation of the observer design may make it insensitive to changes in minor faults. If the reconstruction capability of the observer is not enough to capture the minor state changes caused by minor faults, then these faults may be ignored. In addition, the method of designing an observer may involve complex mathematical operations and derivations, which increases the difficulty of algorithm design.

[0098] As can be seen from Figures 3A-3D, when no auxiliary signal addition is used, the two polytopes representing the state feasible sets of the normal and fault models intersect. The system state estimation and fault isolation results at different times show that during operation, it is impossible to determine whether the buck converter is in a normal or faulty state.

[0099] As can be observed in Figures 4A-4D, after introducing the auxiliary signals derived using existing methods, the two polytopes representing the state feasible sets of the normal and faulty models are not completely separated. This indicates that in some cases, traditional auxiliary signal design methods may not be able to effectively isolate faults. This is due to the traditional methods' insufficient sensitivity to minor faults. In such cases, the system may continue to experience easily overlooked faults during operation, leading to serious industrial problems.

[0100] It can be observed from Figures 5A to 5D that after adding appropriate auxiliary signals using the method provided in the present application, the polyhedrons representing the state feasible sets of the normal model and the fault model can be effectively separated, the state deviation is within an acceptable range, and minor faults can also be quickly diagnosed and effectively separated. This is because the auxiliary signal obtained by the active fault isolation method of power battery formation equipment based on polyhedral filtering and auxiliary signal design proposed in the present application during fault isolation is obtained by solving two optimization problems converted from the equivalent conditions under which the normal model and the fault model can be completely separated. The effectiveness of the auxiliary signal is ensured without relying too much on the model accuracy; in addition, the addition of the auxiliary signal does not change the shape of the original state polyhedron or affect its properties, and does not affect the normal operating state of the equipment.

[0101] 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.

[0102] The above description is only a preferred embodiment of the present invention and is 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 scope of protection of the present invention.

Claims

1. A method for actively isolating power battery formation equipment faults, characterized in that: The method is implemented based on polyhedral filtering and auxiliary signal design, and includes: Step S1: Establishing a power battery formation equipment model including unknown but bounded noise and faults; Step S2: Design a fully symmetric polyhedron observer to estimate the state at the next moment k based on the state at the current moment k-1, thereby obtaining a fully symmetric polyhedron expression that encapsulates the true state at the moment k. is the corresponding model index, ξ represents the fault sequence number; when i = 0, the model is in a normal state, indicating that the power battery formation equipment is operating normally; otherwise, the model is in a fault state, indicating that the power battery formation equipment is faulty; Step S3: Obtaining a measurement state set according to the power battery formation equipment model Expressions of Step S4: Based on the real state at time k Located in a fully symmetric polyhedron and measurement state set The auxiliary signal h used to separate the normal model and the fault model is k The design problem is transformed into the following optimization problem: represents the optimal polytopic outer bound containing the system state at time k in the normal model, represents the optimal polytopic external bound containing the system state at time k in the fault model; Step S5: Find the normal model by adding test points and fault models The equivalent condition of the intersection being empty forms two different optimization problems. Solving the two optimization problems separately will yield two different auxiliary signals h1 and h2. Step S6: Select one of the auxiliary signals h1 and h2 as the final auxiliary signal h k Introducing a battery-powered power conversion equipment state model to proactively isolate faults that occur while the equipment is operating; The power battery formation equipment model established in step S1 includes: Step S1.1, simplifying the power battery formation equipment into a buck converter circuit analysis to obtain the corresponding buck converter circuit model; Step S1.2: Discretize the buck converter circuit model to obtain a power battery formation device state model that includes unknown but bounded noise and faults: in, Represents the state of the i-th model at time k, with the capacitor voltage U in the buck converter circuit c (t) and the inductor current I L (t) as the state of the model; represents the input of the i-th model at time k, i.e., the charge / discharge current and / or charge / discharge voltage; represents the output of the i-th model at time k, which is the load battery voltage or load battery capacity; A [i] Represents the known state transfer matrix, B [i] Represents the known input matrix, C [i] represents the known output matrix; represents the unknown but bounded disturbance noise at time k; represents the unknown but bounded measurement noise at time k; h k Indicates the auxiliary signal.

2. The method according to claim 1, characterized in that In step S2, the expression for estimating the state at the next moment k based on the state at the current moment k-1 is: in, That is, corresponding to the i-th model, the state at the next moment k is estimated based on the state at the current moment k-1; It indicates that the center of the fully symmetric polyhedron predicted at time k by the i-th model based on the prediction at time k-1, represents the matrix generated by this polyhedron; Is a combination of r+n x unit box composed of unit intervals; r corresponds to the dimension of the fully symmetric polyhedron, n x corresponds to the dimension of the perturbation noise.

3. The method according to claim 2, characterized in that The step S3 comprises: According to the power battery formation equipment state model shown in formula (1), the output of the i-th model at time k is derived as follows: Then we get the measurement state set The expression: Where σ represents the relative coefficient of the central interval of the measurement noise relative to the unit interval.

4. The method according to claim 3, characterized in that In step S4, the real state at time k Located in a fully symmetric polyhedron With measuring tape Inside the intersection, the optimal polytope outside the intersection is bounded The center and generator matrix are: in I represents the identity matrix and T represents the transpose operation.

5. The method according to claim 4, characterized in that The step S5 comprises: For λ1,λ2∈[0,1] that satisfy the following equations (10) and (11): like: or Comprehensively consider equations (12), (13) and λ1, λ2, The prior condition of , the optimization problem described by formula (9) is comprehensively expressed as: or Solve the two different optimization problems in equation (14) and equation (15) respectively, and obtain two different auxiliary signals h1 and h2.

6. The method according to claim 5, characterized in that The step S6 comprises: The auxiliary signal h is finally selected according to the following rules k : h k =min{|h1|,|h2|} (16) The auxiliary signal h k The battery-to-power conversion equipment state model is introduced to complete the active isolation of faults that occur when the equipment is in operation.

7. The method according to claim 6, characterized in that The buck converter circuit model obtained in step S1.1 is: Among them, R load , L, C, and D are the load resistance, inductance, capacitance, and duty cycle parameters of the buck converter circuit respectively; select the capacitor voltage U c (t) and the inductor current I L (t) is the state of the system, so x(t) = [x1(t)x2(t)] T =[I L (t)U c (t)] T ; Input voltage E(t) and load current I load (t) is the external input variable of the system, and u(t)=[E(t)I load (t)] T .

8. The method according to claim 7, characterized in that Select the output matrix C [i] When [01], the output variable y(t) = U c (t).

9. A method for monitoring the operation of a power battery formation equipment, characterized in that: The method adopts the method according to any one of claims 1 to 8 to actively isolate faults occurring during the operation of power battery formation equipment.

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