Fault detection isolation and positioning method for general battery system

Abstract

The invention provides a fault detection, isolation and positioning method for a general battery system, and the method can achieve the detection, isolation and positioning of various types of faults, such as a voltage sensor fault, a current sensor fault, a poor contact, a battery cell fault, and the like, only through the utilization of voltage and current data which are commonly used at present. Modeling and calculation processes are simple and convenient, and additional disassembly and transformation and sensor installation of the battery system are not needed, so that multiple simple, efficient and reliable comprehensive fault diagnosis solutions are provided for a general battery system represented by a lithium ion battery.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery system safety status monitoring and fault diagnosis, and in particular to a technology for comprehensive detection, isolation and positioning of multiple faults in a large-scale series-parallel grouped battery system. Background Art

[0002] At present, for complex battery systems composed of a large number of battery cells and branches, internal faults cannot be completely avoided. The causes of various faults and the electrical coupling phenomena between cells are still not fully understood in this field. Therefore, fault diagnosis technology plays a vital role in the reliability management of battery systems. Accurate fault diagnosis can not only ensure the safe operation of the battery system, but also effectively reduce the risk of thermal runaway fire accidents caused by faults. Most of the existing fault diagnosis for general battery systems is based on threshold detection. This method often makes it difficult to accurately and quickly detect and isolate faults at the system level using limited sensor information, which makes it difficult to quickly and accurately handle faults, greatly increasing the safety risk of the system. In addition, current fault diagnosis methods are generally only designed for a single fault type, and lack systematic fault modeling and isolation design, so they are prone to false alarms in actual use. Summary of the invention

[0003] In view of this, in view of the technical problems existing in the art, the present invention provides a method for fault detection, isolation and positioning of a universal battery system, which specifically includes the following steps:

[0004] Step 1: Obtain the battery system structural parameters of the battery system under test, including the number of battery cells and the series-parallel configuration, as well as the open circuit voltage of the battery cells and the model parameters used for modeling the battery cells and the system;

[0005] Step 2: Select a suitable model to model the voltage and current characteristics of the battery cell; and according to the established battery cell model and battery system composition structure, build a residual-based battery system fault diagnosis model to calculate the residuals corresponding to the battery cell, branch and the entire battery system, and use the combination of three-level residuals to establish a mapping relationship corresponding to different fault types;

[0006] Step 3: Initialize the fault diagnosis process, including setting the initial state of the battery cell and the system including OCV, SOC, etc., the algorithm parameters for battery cell state estimation, and setting time t=0;

[0007] Step 4: Let t = t + 1, execute the state estimation algorithm to update the state variables of the battery cells, and calculate parameters such as OCV, SOC, and polarization voltage;

[0008] Step 5: Utilize the parameters calculated in step 4 and input them into the fault diagnosis model established in step 2 to calculate the third-level residual; by comparing each residual with a given threshold, each residual is converted into a logical quantity containing only 0 and 1, which is defined as a residual flag quantity;

[0009] Step 6: Use the combination of residual flags to determine whether a fault occurs. If all elements of the residual flags are 0, no fault occurs, and the process returns to step 4. If there are non-zero elements in the residual flags, the process proceeds to step 7.

[0010] Step 7: Use the mapping relationship obtained in step 2 to convert it into a fault mapping table corresponding to different residual flag combinations, obtain the fault type and positioning result by looking up the table, output the fault diagnosis conclusion and return to step 4.

[0011] Furthermore, in step one, the influence of the correction temperature, aging, and SOC change on other parameters is also obtained to improve the accuracy of subsequent modeling.

[0012] Furthermore, in step 2, when modeling the battery cell, an equivalent circuit model is selected that can at least describe its current power and polarization state, such as SOC, OCV, polarization voltage, etc.;

[0013] The specific form of defining the third-level residual r is as follows:

[0014] r=[r 1 ,r 2 ,r 3 ]

[0015] Among them, the first element monomer residual r 1 It is a p×s matrix, where p is the number of parallel branches and s is the number of series monomers on each branch. Therefore, r 1 One-to-one correspondence with each monomer in the battery system; the second element branch residual r 2 is a vector of length p, corresponding to each branch one by one; the third element is the system residual r 3 is a scalar; the elements of the residual are calculated as follows:

[0016]

[0017] Where k is the time, the subscript i indicates the branch to which it belongs, i = 1, 2, 3, ..., p, and the subscript j indicates the jth monomer on a branch, j = 1, 2, 3, ..., s; OCV, R, U D They are the open circuit voltage, ohmic internal resistance, polarization voltage, and y I ,y U ,y Ii ,y Uij They are the sensor readings of total current, total voltage, branch current and single cell voltage.

[0018] Furthermore, in step 4, the real-time voltage and current data are used as inputs of the state estimation algorithm, and the state estimation is performed with Δt as the sampling interval, and the estimation result is corrected using the estimation error feedback link.

[0019] Furthermore, in step six, after the three-level residual calculation and comparison with the given threshold, a residual marker combination r' containing only 0 and 1 elements is obtained; in step seven, in the mapping table obtained by converting the mapping relationship between the combination of the three-level residuals and the different fault types, the fault feature f with the same combination form is searched, thereby realizing the diagnosis of the fault location of different fault types.

[0020] The fault detection, isolation and positioning method for universal battery systems provided by the present invention only needs to utilize the currently commonly used voltage and current data to realize the detection, isolation and positioning of various types of faults including voltage sensor fault, current sensor fault, poor contact, battery cell fault, etc. The modeling and calculation process is relatively simple, and there is no need for additional disassembly and modification of the battery system and the installation of sensors, thereby providing a simple, efficient and reliable variety of comprehensive fault diagnosis solutions for universal battery systems represented by lithium-ion batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of the process provided by the present invention;

[0022] Figure 2 The following are the diagnosis results of a voltage sensor failure on a specific branch or cell according to an example of the present invention. DETAILED DESCRIPTION

[0023] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] The present invention provides a method for fault detection, isolation and location of a universal battery system, such as Figure 1 As shown, the specific steps include:

[0025] Step 1: Obtain the battery system structural parameters of the battery system under test, including the number of battery cells and the series-parallel configuration, as well as the open circuit voltage of the battery cells and the model parameters used for modeling the battery cells and the system;

[0026] Step 2: Select a suitable model to model the voltage and current characteristics of the battery cell; and according to the established battery cell model and battery system composition structure, build a residual-based battery system fault diagnosis model to calculate the residuals corresponding to the battery cell, branch and the entire battery system, and use the combination of three-level residuals to establish a mapping relationship corresponding to different fault types;

[0027] Step 3: Initialize the fault diagnosis process, including setting the initial state of the battery cell and the system including OCV, SOC, etc., and the algorithm parameters for battery cell state estimation, such as the process noise covariance matrix and measurement noise covariance matrix related to Kalman filtering, and set time t=0;

[0028] Step 4: Let t = t + 1, execute the state estimation algorithm to update the state variables of the battery cells, and calculate parameters such as OCV, SOC, and polarization voltage;

[0029] Step 5: Utilize the parameters calculated in step 4 and input them into the fault diagnosis model established in step 2 to calculate the third-level residual; by comparing each residual with a given threshold, each residual is converted into a logical quantity containing only 0 and 1, which is defined as a residual flag quantity;

[0030] Step 6: Use the combination of residual flags to determine whether a fault occurs. If all elements of the residual flags are 0, no fault occurs, and the process returns to step 4. If there are non-zero elements in the residual flags, the process proceeds to step 7.

[0031] Step 7: Use the mapping relationship obtained in step 2 to convert it into a fault mapping table corresponding to different residual flag combinations, obtain the fault type and positioning result by looking up the table, output the fault diagnosis conclusion and return to step 4.

[0032] In a preferred embodiment of the present invention, the above method is used to diagnose faults in a 2-series 2-parallel lithium-ion battery system. The two branches of the 2-series 2-parallel battery system are respectively denoted as s and p, and the four battery cells are respectively denoted as s1, s2, p1, and p2.

[0033] In step one, the influence of the correction temperature, aging, and SOC change on other parameters is also obtained to improve the accuracy of subsequent modeling.

[0034] A second-order RC equivalent circuit model was established for the battery cell;

[0035] The specific form of defining the third-level residual r is as follows:

[0036] r=[r 1 ,r 2 ,r 3 ]

[0037] Among them, the first element monomer residual r 1 It is a p×s matrix, where p is the number of parallel branches and s is the number of series monomers on each branch. Therefore, r 1 One-to-one correspondence with each monomer in the battery system; the second element branch residual r 2 is a vector of length p, corresponding to each branch one by one; the third element is the system residual r 3 is a scalar; the elements of the residual are calculated as follows:

[0038]

[0039] Where k is the time, the subscript i indicates the branch to which it belongs, i = 1, 2, 3, ..., p, and the subscript j indicates the jth monomer on a branch, j = 1, 2, 3, ..., s; OCV, R, U D They are the open circuit voltage, ohmic internal resistance, polarization voltage, and y I ,y U ,y Ii ,y Uij They are the sensor readings of total current, total voltage, branch current and single cell voltage.

[0040] In step 4, the real-time voltage and current data are used as the input of the state estimation algorithm, the Kalman filter algorithm is selected, and the state estimation is performed with Δt as the sampling interval, and the estimation result is corrected using the estimation error feedback link.

[0041] Step 6: After the three-level residual calculation and comparison with the given threshold, a residual mark quantity combination r' containing only 0 and 1 elements is obtained; Step 7: In the mapping table obtained by converting the mapping relationship between the combination of the three-level residuals and different fault types, the fault feature quantity f with the same combination form is searched, that is, the diagnosis of the fault location of different fault types is realized. In this embodiment, the mapping table shown in Table 1 below is specifically used:

[0042] Table 1 Fault type, location and residual sign mapping table

[0043]

[0044]

[0045] By looking up the table, in this embodiment, it is successfully found that the voltage sensor fault exists in the first cell of branch s, as shown in the following example. Figure 2 shown.

[0046] It should be understood that the size of the serial numbers of the steps in the embodiment of the present invention does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0047] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for fault detection, isolation and location for a universal battery system, characterized in that: The specific steps include: Step 1: Obtain the battery system structural parameters of the battery system under test, including the number of battery cells and the series-parallel configuration, as well as the open circuit voltage of the battery cells and the model parameters used for modeling the battery cells and the system; Step 2: Select a suitable model to model the voltage and current characteristics of the battery cell; and according to the established battery cell model and battery system composition structure, build a residual-based battery system fault diagnosis model to calculate the residuals corresponding to the battery cell, branch and the entire battery system, and use the combination of three-level residuals to establish a mapping relationship corresponding to different fault types; Step 3: Initialize the fault diagnosis process, including setting the initial state of the battery cell and the system including OCV and SOC, the algorithm parameters for battery cell state estimation, and setting time t=0; Step 4: Let t=t+1, execute the state estimation algorithm to update the state variables of the battery cells, and calculate the OCV, SOC, and polarization voltage parameters; Step 5: Utilize the parameters calculated in step 4 and input them into the fault diagnosis model established in step 2 to calculate the third-level residual; by comparing each residual with a given threshold, each residual is converted into a logical quantity containing only 0 and 1, which is defined as a residual flag quantity; Step 6: Use the combination of residual flags to determine whether a fault occurs. If all elements of the residual flags are 0, no fault occurs, and the process returns to step 4. If there are non-zero elements in the residual flags, the process proceeds to step 7. Step 7: Use the mapping relationship obtained in step 2 to convert it into a fault mapping table corresponding to different residual flag combinations, obtain the fault type and positioning result by looking up the table, output the fault diagnosis conclusion and return to step 4.

2. The method according to claim 1, characterized in that: In step one, the influence of the correction temperature, aging, and SOC change on other parameters is also obtained to improve the accuracy of subsequent modeling.

3. The method according to claim 1, characterized in that: In step 2, when modeling the battery cell, an equivalent circuit model is selected that can at least describe its current power and polarization state, such as SOC, OCV, and polarization voltage; The specific form of defining the third-level residual r is as follows: r＝[r1,r2,r3] Among them, the first element, the monomer residual r1, is a p×s matrix, p is the number of parallel branches, and s is the number of series monomers on each branch, so r1 corresponds to each monomer in the battery system one by one; the second element, the branch residual r2, is a vector of length p, corresponding to each branch one by one; the third element, the system residual r3, is a scalar; the calculation formula for each element in the residual is as follows: Where k is the time, the subscript i indicates the branch to which it belongs, i = 1, 2, 3, ..., p, and the subscript j indicates the jth monomer on a branch, j = 1, 2, 3, ..., s; OCV, R, U D They are the open circuit voltage, ohmic internal resistance, polarization voltage, and y I ,y U ,y Ii ,y Uij They are the sensor readings of total current, total voltage, branch current and single cell voltage.

4. The method according to claim 1, characterized in that: In step 4, the real-time voltage and current data are used as the input of the state estimation algorithm, and the state estimation is performed with Δt as the sampling interval, and the estimation result is corrected using the estimation error feedback link.

5. The method according to claim 1, characterized in that: In step 6, after the three-level residual calculation and comparison with the given threshold, a residual marker combination r' containing only 0 and 1 elements is obtained; in step 7, in the mapping table obtained by converting the mapping relationship between the combination of the three-level residuals and different fault types, the fault feature f with the same combination form is searched, thus realizing the diagnosis of the fault location of different fault types.

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