TWI749835B - Battery diagnosis apparatus, method, and computer program product thereof - Google Patents

Abstract

A battery diagnosis apparatus, method, and computer program product thereof are provided. The battery diagnosis apparatus stores a fault analysis tree, a plurality of protection parameters of a battery module, and a plurality of internal parameters of the battery module. The battery diagnosis apparatus determines a protection status and a fault type of the battery module according to at least one of the protection parameters. The battery diagnosis apparatus queries the fault analysis tree according to the protection status and the fault type to derive at least one candidate fault cause. The battery diagnosis apparatus determines that a subset of the internal parameters conforms to a determination rule of one of the at least one candidate fault cause and thereby determine that one of the at least one candidate fault cause is a rough fault cause of the battery module.

Description

Battery diagnostic device, method and computer program product

The invention relates to a battery diagnostic device, method and computer program product. Specifically, the present invention relates to a battery diagnostic device, method and computer program product using a hierarchical diagnostic architecture.

With the rapid development of electronic technology, the demand for power supply devices (such as battery modules) in various electronic products continues to increase. When users want to evaluate the various states of the battery module, they usually send the battery module back to the dealer for assistance. However, most dealers can only read the status parameters of the battery module through general battery diagnostic tools. If the battery module is judged to have an abnormal state from these status parameters, it is still difficult to check the various abnormal states of the battery module. Disposal. The user still needs to return the battery module to the original factory, relying on the original factory's professionals to further understand the actual cause of the failure, and then eliminate the abnormal state.

In view of this, providing a battery diagnostic technology that allows users to easily diagnose the status of the battery module and immediately analyze various abnormal conditions of the battery module to provide the user with the cause of the battery module failure is an urgent need in the industry. Target.

An object of the present invention is to provide a battery diagnostic device. The battery diagnostic device includes a storage and a processor, wherein the storage is electrically connected with the processor. This memory stores a story Fault analysis tree, a plurality of protection parameters of a battery module, and a plurality of internal parameters of the battery module. The processor determines a protection state and a fault type of the battery module according to at least one of the protection parameters, queries the fault analysis tree according to the protection state and the fault type to obtain at least one candidate fault cause, and judges A first subset of the internal parameters meets a judgment rule corresponding to one of the at least one candidate failure cause, thereby confirming that one of the at least one candidate failure cause is a preliminary failure cause of the battery module.

In some battery diagnostic devices provided by the present invention, the processor can also input a second subset of the internal parameters into a machine learning model corresponding to the preliminary failure cause to confirm a specific failure cause of the battery module .

Another object of the present invention is to provide a battery diagnosis method, which is suitable for an electronic computing device. The electronic calculation device stores a fault analysis tree, a plurality of protection parameters of a battery module, and a plurality of internal parameters of the battery module. The battery diagnosis method includes the following steps: (a) determining a protection state of the battery module according to at least one of the protection parameters, (b) determining a protection state of the battery module according to one of the protection parameters Fault type, (c) query the fault analysis tree according to the protection status and the fault type to obtain at least one candidate fault cause, and (d) determine that a first subset of the internal parameters meets the at least one candidate fault cause A judgment rule corresponding to one of them is used to confirm that one of the at least one candidate failure cause is a preliminary failure cause of the battery module.

Some battery diagnosis methods provided by the present invention may further include a step of inputting a second subset of the internal parameters to a machine learning model corresponding to the preliminary failure cause to confirm a specific failure cause of the battery module .

Another object of the present invention is to provide a computer program product. After loading the computer program product through an electronic computing device, the electronic computing device executes the copy contained in the computer program product Several program instructions to perform a battery diagnostic method. The electronic calculation device stores a fault analysis tree, a plurality of protection parameters of a battery module, and a plurality of internal parameters of the battery module. The battery diagnosis method includes the following steps: (a) determining a protection state of the battery module according to at least one of the protection parameters, (b) determining a protection state of the battery module according to one of the protection parameters Fault type, (c) query the fault analysis tree according to the protection status and the fault type to obtain at least one candidate fault cause, and (d) determine that a first subset of the internal parameters meets the at least one candidate fault cause A judgment rule corresponding to one of them is used to confirm that one of the at least one candidate failure cause is a preliminary failure cause of the battery module.

In some computer program products provided by the present invention, the program instructions contained in it can also enable the electronic computing device to execute a step to input a second subset of the internal parameters into a machine corresponding to the initial failure cause Learn the model to confirm a specific failure cause of the battery module.

The battery diagnostic technology (including devices, methods and computer program products) provided by the present invention analyzes whether the battery module is abnormal according to multiple protection parameters of the battery module, and determines which battery module needs to be activated Protection state (for example: unrecoverable state, first recoverable state or second recoverable state), and the fault type of the battery module (for example: hardware fault type, software/firmware fault type ). The battery diagnosis technology provided by the present invention queries a fault analysis tree according to the protection status and the fault type to obtain at least one candidate fault cause, for example, the voltage of a certain battery is abnormal. The battery diagnosis technology provided by the present invention then compares one or more of the at least one candidate failure cause to determine whether a first subset of the internal parameters of the battery module corresponds to the candidate failure cause A judgment rule for confirming that one of the at least one candidate failure cause is a preliminary failure cause of the battery module. The battery diagnostic technology provided by the present invention can also A second subset of internal parameters is input to a machine learning model corresponding to the preliminary failure cause, thereby confirming a specific failure cause of the battery module.

Through the foregoing operations/steps, the battery diagnosis technology provided by the present invention realizes the hierarchical diagnosis of the battery module-analyzes the protection status of the battery module, the type of failure, and the preliminary cause of the failure at different levels, and can even analyze The specific cause of the failure of the battery module. Therefore, the user can learn the various states of the battery module, various abnormal states of the battery module, and the cause of the failure of the battery module through the battery diagnostic technology provided by the present invention without returning the battery to the original factory, which is extremely convenient.

The detailed technology and implementation manners of the present invention are described below in conjunction with the drawings, so that those with ordinary knowledge in the technical field to which the present invention belongs can understand the technical features of the claimed invention.

1: Battery diagnostic device

11: Storage

13: processor

20: Fault analysis tree

30, 40, 50: Correspondence table of fault type

IP: internal parameters

PP: protection parameter

PFS: Permanent fail-safe state parameter

SS1: First safety protection status parameter

SS2: The second safety protection status parameter

S201~S209: steps

FIG. 1A is a schematic diagram depicting the structure of the battery diagnostic device 1 of the first embodiment.

Figure 1B is a schematic diagram depicting a specific example of the fault analysis tree of the present invention.

FIG. 1C is a schematic diagram depicting how the battery diagnostic device 1 determines the protection state of the battery module.

FIG. 1D is a schematic diagram depicting a specific example of how the battery diagnostic device 1 determines a fault type of the battery module.

FIG. 1E is a schematic diagram depicting a specific example of hierarchical diagnosis performed by the battery diagnosis device 1.

FIG. 1F is a schematic diagram depicting another specific example of hierarchical diagnosis performed by the battery diagnosis device 1.

Figure 2A depicts the main flow chart of the second embodiment of the present invention.

Figure 2B is a flowchart depicting certain embodiments of the present invention.

The following will explain the battery diagnostic device, method and computer program product provided by the present invention through implementations. However, these embodiments are not intended to limit the implementation of the present invention in any environment, application or method as described in these embodiments. Therefore, the description of the following embodiments is only for explaining the purpose of the present invention, not for limiting the scope of the present invention. It should be understood that, in the following embodiments and drawings, elements not directly related to the present invention have been omitted and not shown. In addition, the size of each element and the size ratio between the elements in the drawings are only for ease of illustration and description, and are not used to limit the scope of the present invention.

The first embodiment of the present invention is the battery diagnostic device 1, and its schematic diagram is depicted in FIG. 1A. The battery diagnostic device 1 includes a storage 11 and a processor 13, wherein the storage 11 and the processor 13 are electrically connected. The storage 11 can be a memory, a Universal Serial Bus (USB) disc, a hard disk, a compact disk (CD), a Digital Versatile Disc (DVD), A flash drive or any other non-transitory storage medium or storage circuit capable of storing digital data known to those with ordinary knowledge in the technical field of the present invention. The processor 13 can be a variety of processors, central processing units (CPU), microprocessors (MPU), digital signal processors (DSP), or those commonly used in the technical field of the present invention. Other computing devices known to the knowledgeable.

In this embodiment, the storage 11 of the battery diagnostic device 1 stores a fault analysis tree 20. Specifically, in order to provide battery diagnosis similar to battery experts (for example: maintenance engineers of battery manufacturers), it is necessary to collect the expert experience of battery experts first, and aggregate it into a fault analysis tree 20, so that the fault analysis tree 20 contains Multiple levels corresponding to the diagnosis results of various faulty battery modules. For ease of understanding, please refer to a specific example of the fault analysis tree 20 shown in FIG. 1B, which will be used in the subsequent description, but is not intended to limit the scope of the present invention. In this specific example, the fault analysis tree 20 has a first Layer, a second layer, a third layer, and a fourth layer. The first layer summarizes the various protection states of the faulty battery module, the second layer summarizes the various fault types of the faulty battery module, and the third The layer summarizes various preliminary failure causes of the failed battery module, and the fourth layer summarizes various specific failure causes of the failed battery module.

In this embodiment, in accordance with the hierarchical structure of the fault analysis tree 20, the diagnosis performed by the battery diagnosis device 1 can be divided into three diagnosis stages, or even four diagnosis stages. The first diagnosis stage is used to diagnose a protection state of a battery module, the second diagnosis stage is used to diagnose a fault type of the battery module, and the third diagnosis stage is used to diagnose a preliminary fault of the battery module. The reason, and the fourth diagnosis stage is used to diagnose a specific failure reason of the battery module.

In this embodiment, the memory 11 of the battery diagnostic device 1 stores a plurality of protection parameters PP and a plurality of internal parameters IP of a failed battery module (not shown) that needs to be diagnosed. The protection parameter PP refers to various protection record values in the battery module, such as a permanent failure protection status parameter (Permanent Failure Status), a first safety protection status parameter (Safety Status), and a second safety protection status parameter. The internal parameter IP refers to various other recorded values in the battery module, such as: the firmware version of the battery module, the time of failure, the current value of each battery cell of the battery module, and the voltage of each battery cell of the battery module Value, the temperature of each battery cell of the battery module, etc. It should be noted that the protection parameter PP and the internal parameter IP can be obtained from the battery module to be diagnosed through a battery diagnostic tool (Diagnostic Tool) provided by the manufacturer of the battery module. For example, the user can electrically connect the battery diagnostic tool to the battery module, and use a diagnostic software to access the protection parameter PP and internal parameter IP of the battery module, and then export it in Excel file format for convenience Follow-up battery diagnosis and management.

Hereinafter, in conjunction with FIG. 1C and FIG. 1D, an example will be given to illustrate how the battery diagnostic device 1 performs the first and second diagnostic stages described above. It should be noted that the content shown in FIG. 1C and FIG. 1D is only an example, and is not intended to limit the protection scope of the present invention.

As shown in Figure 1C, in this embodiment, the protection parameter PP includes a permanent fail protection state parameter PFS, a first safety protection state parameter SS1, and a second safety protection state parameter SS2, and the processing of the battery diagnostic device 1 The device 13 determines a protection state of the battery module according to at least one of the protection parameters PP. It should be noted that when the battery module starts to operate, the permanent failure protection state parameter PFS, the first safety protection state parameter SS1, and the second safety protection state parameter SS2 are all 0. If a fault occurs during the operation of the battery module, the permanent fail-safe state parameter PFS, the first safety protection state parameter SS1 or/and the second safety protection state parameter will become a positive integer (described later).

Please refer to Figure 1C. The processor 13 first makes a judgment according to the permanent fail-safe state parameter PFS. If the processor 13 determines that the permanent failure protection state parameter PFS is greater than 0, the processor 13 will determine that the protection state of the battery module is an unrecoverable state (that is, the battery module has been permanently damaged) based on the determination result. If the processor 13 determines that the permanent failsafe state parameter PFS is not greater than 0 (that is, the permanent failsafe state parameter PFS is equal to 0), the processor 13 further makes a judgment according to the first safety protection state parameter SS1. If the processor 13 determines that the permanent failure protection state parameter PFS is equal to 0 and the first safety protection state parameter SS1 is greater than 0, the processor 13 will determine that the protection state of the battery module is a first recoverable state based on the result of this determination. The first recoverable state is related to a type of software/firmware failure, and the battery module can be used again after repair. If the processor 13 determines that the permanent fail-safe state parameter PFS is equal to 0 but the first safety-protected state parameter SS1 is not greater than 0 (that is, the first safety-protected state parameter SS1 is equal to 0), the processor 13 further follows the second safety-protected state Parameter SS2 is judged. If the processor 13 determines that the permanent fail-safe state parameter PFS is equal to 0, the first safe-protected state parameter SS1 is equal to 0, and the second safe state parameter If the full protection parameter is greater than 0, the processor 13 will determine the protection state of the battery module as a second recoverable state based on the judgment result, wherein the second recoverable state is related to a hardware failure type, and the battery The module can be used again after repair.

In this embodiment, if the processor 13 determines that the protection state of the battery module is an unrecoverable state, the processor 13 will also query the corresponding fault type correspondence table 30 according to the permanent fail protection state parameter PFS (please refer to 1D). Figure) to determine a fault type of the battery module. Specifically, the permanent failsafe state parameter PFS includes a plurality of bits (for example: 8 bits in the high byte and 8 bits in the low byte), and some of these bits correspond to specific fault types As shown in Figure 1D. The bits of the permanent fail-safe state parameter PFS are interpreted as an unsigned integer (Unsigned Integer) to be the value of the permanent fail-safe state parameter PFS. For example, if the content of 16 bits included in the permanent fail-safe state parameter PFS is 0000 0001 0000 0000, the hexadecimal value of the permanent fail-safe state parameter PFS is 100. The processor 13 can query the fault type correspondence table 30 (specifically, query the fault type corresponding to the bit whose bit value is 1) according to the content of the 16 bits contained in the permanent fail-safe state parameter PFS, and determine the The fault type is exceeding the service life or maximum charge capacity of the battery module. For another example, if the content of 16 bits included in the permanent fail-safe state parameter PFS is 0000 0000 0000 0001, the hexadecimal value of the permanent fail-safe state parameter PFS is 1. The processor 13 can query the fault type correspondence table 30 according to the 16-bit content contained in the permanent fail-safe state parameter PFS, and determine that the fault type is the unbalanced battery cell voltage of the battery module.

Similarly, if the processor 13 determines that the protection state of the battery module is the first recoverable state, the processor 13 will query the corresponding other fault type correspondence table 40 according to the first safety protection state parameter SS1 to determine The fault type of the battery module. Similarly, the first safety protection state parameter SS1 includes a plurality of bits, and some of the bits correspond to specific fault types. Therefore, the processor 13 can According to the content of the bit contained in the first safety protection state parameter SS1, the corresponding fault type correspondence table is queried (specifically, the fault type corresponding to the bit whose bit value is 1 is queried), and the fault type is determined.

Similarly, if the processor 13 determines that the battery module is in the second recoverable state, the processor 13 will query the corresponding table 50 for another fault type according to the second safety protection state parameter SS2, thereby determining the battery module The type of failure.

The third diagnosis stage of the battery diagnosis device 1 will now be described. The processor 13 queries the fault analysis tree 20 according to the protection state and the fault type to obtain at least one candidate fault cause. It should be noted that since the first level of the fault analysis tree 20 summarizes the various protection states of the failed battery modules, the second level summarizes the various fault types of the failed battery modules, and the third level summarizes the failed battery modules. The fourth level summarizes various specific failure causes of the failed battery module. Therefore, the processor 13 can query the fault analysis tree 20 according to the protection state of the battery module and the failure type to obtain at least A candidate failure reason. For example, if the protection state of the battery module is an unrecoverable state and the fault type of the battery module is too low voltage, the processor 13 will query the fault analysis tree 20 and obtain candidate fault causes such as abnormal voltage and abnormal charging (please (Refer to Figure 1B). For another example, if the protection state of the battery module is the first recoverable state and the fault type of the battery module is abnormal temperature, the processor 13 will query the fault analysis tree 20 to obtain the abnormality of the first temperature sensor and the abnormality of the first temperature sensor. 2. Candidate failure reasons such as abnormal temperature sensor (please refer to Figure 1B).

Next, the processor 13 determines whether any of the at least one candidate failure cause can be used as the preliminary failure cause of the battery module. Specifically, each candidate failure cause corresponds to a judgment rule, so the processor 13 judges whether a subset of the internal parameter IP of the battery module meets the judgment rule corresponding to a certain candidate failure cause. If the processor 13 determines that a subset of the internal parameter IP of the battery module meets a certain candidate The judgment rule corresponding to the failure cause is selected, and the processor 13 determines that the candidate failure cause is the preliminary failure cause of the battery module.

In some embodiments, if the processor 13 obtains a plurality of candidate failure causes (that is, according to the protection state of the battery module and the failure type, a plurality of candidate preliminary failures are found in the third level of the fault analysis tree 20 Cause), the processor 13 can determine whether each candidate failure cause can be used as a preliminary failure cause of the battery module according to a judgment sequence among the candidate failure causes. The aforementioned judgment sequence can be set by the manufacturer based on the probability of failure, or based on the professional experience of experts in the battery field. Specifically, the processor 13 determines one by one whether each candidate failure cause can be used as a preliminary failure cause of the battery module according to the determination sequence. Once the processor 13 determines that a subset of the internal parameter IP of the battery module meets a certain candidate failure A judgment rule corresponding to the cause confirms that the candidate fault cause is a preliminary fault cause of the battery module, and no longer judges other candidate fault causes that have not yet been judged.

In some embodiments, after the processor 13 confirms the preliminary failure cause of the battery module, the processor 13 may also perform a fourth diagnosis stage to confirm a specific failure cause of the battery module. Please refer to Figure 1B. Each preliminary failure cause in the third layer of the fault analysis tree 20 should be one or more specific failure causes (for example: the preliminary failure cause "Voltage Abnormality" corresponds to the specific failure cause "Improper battery classification", "Battery Poor assembly" etc.). In these embodiments, the processor 13 or the processor of other devices can individually train a machine learning model for each of the preliminary failure causes in Figure 1B. It should be clarified that a person with general knowledge in the technical field of the present invention should be familiar with how to train a machine learning model, so it will not be repeated. Since each preliminary failure cause in Figure 1B corresponds to a machine learning model, the processor 13 can input another subset of the internal parameter IP of the battery module to the machine corresponding to the preliminary failure cause of the battery module. Learn the model to confirm a specific failure cause of the battery module. If a preliminary fault cause should be When multiple specific failure causes are reached, the machine learning model will output the probability of each specific failure cause, and the processor 13 can select the specific failure cause with the highest probability value as the specific failure cause of the battery module.

To facilitate the understanding of how the battery diagnostic device 1 diagnoses the preliminary failure cause and the specific failure cause, please refer to the specific example shown in Figure 1E. In this specific example, the processor 13 has diagnosed that the protection state of the battery module is the unrecoverable state, and the fault type of the battery module is that the voltage of the battery module is too low. Accordingly, the processor 13 queries the fault analysis tree 20 to obtain five candidate failure causes, including voltage abnormalities (that is, as shown in the figure, "check the cell voltage value through the status list to see if there are a few voltage values too low. ..”), six months of non-charge (ie, "whether it has not been charged for six months..." shown in the figure), etc., and there is a judgment sequence among these candidate failure causes. The processor 13 first reads the voltage of the battery module at the moment when the battery module fails according to the judgment sequence, and judges whether it meets the judgment rule corresponding to the abnormal voltage (for example, the voltage of a battery cell in the battery module is much lower than the other The voltage of the battery cell). It is assumed that the battery module has 10 battery cells, and the voltage value of the battery cells at the moment when the battery module fails is a subset of the internal parameter IP of the battery module. The processor 13 determines that the voltage values of the battery cells at the moment when the battery module fails meet the judgment rule corresponding to the abnormal voltage (that is, the voltage value of the No. 10 battery cell is much lower than the voltage values of other battery cells), Therefore, it is confirmed that the cause of the preliminary failure of the battery module is the abnormal voltage of the No. 10 battery cell. The processor 13 queries the fault analysis tree 20 based on this preliminary fault cause, and knows that the possible specific fault cause of the battery module may be improper battery classification or poor battery assembly. The processor 13 may further be a subset of the internal parameter IP that was continuously recorded before the battery module failed (for example: the continuously recorded voltage value and internal resistance of the battery cells before the battery module failed) ) Enter the machine learning model corresponding to the initial failure cause "Voltage Abnormality", and then you can know the probability of improper battery classification and the probability of poor battery assembly, and then select the higher probability as the specific failure cause of the battery module.

Please also refer to the specific example shown in Figure 1F. In this specific example, the processor 13 has diagnosed that the protection state of the battery module is the first recoverable state, and the fault type of the battery module is that the temperature of the battery module is abnormal. Accordingly, the processor 13 queries the fault analysis tree 20 to obtain a candidate fault cause, that is, the temperature is abnormal (that is, the "Temp1 and Temp2 shown in the figure have a large difference..."). In this specific example, the battery module has two series of temperature sensors, and the temperature corresponding to the two series of temperature sensors when the battery module fails is a sub-parameter of the internal parameter IP of the battery module. set. The processor 13 judges that the temperature of the temperature sensors at the moment when the battery module fails meets the judgment rule corresponding to the abnormal temperature (that is, one or some of the temperatures are lower than a preset threshold, which is assumed to be the first The temperature of the second temperature sensor is lower than the preset threshold value), so it is confirmed that the preliminary fault cause is the abnormality of the second temperature sensor. Then, the processor 13 queries the fault analysis tree 20 based on this preliminary fault cause, and knows that the possible specific fault cause of the battery module may be an abnormal algorithm or an abnormal circuit. The processor 13 may further input a subset of those internal parameter IPs that were continuously recorded before the battery module failed (for example: the temperature of the second temperature sensor before the battery module failed) to input the preliminary failure cause The machine learning model corresponding to "abnormal temperature" can obtain the probability of algorithm abnormality and the probability of line abnormality, and then select the higher probability as the specific failure cause of the battery module.

In summary, the battery diagnostic device 1 analyzes whether the battery module is abnormal according to a plurality of protection parameters of the battery module, and determines which protection state (that is, the unrecoverable state, the first A recoverable state or a second recoverable state), and determine what type of failure the battery module belongs to (that is, the type of hardware failure, the type of software/firmware failure). The battery diagnostic device 1 also queries the fault analysis tree 20 according to the protection status and the fault type to obtain at least one candidate fault cause, and determines whether a first subset of the internal parameters of the battery module meets the at least one candidate fault cause A judgment rule corresponding to one of the at least one candidate failure cause One of them is a preliminary failure cause of the battery module. The battery diagnostic device 1 can also input a second subset of the internal parameters of the battery module into a machine learning model corresponding to the preliminary failure cause to confirm a specific failure cause of the battery module. Through the foregoing operations, the battery diagnostic device 1 performs a hierarchical diagnosis of the battery module-analyzes the protection status of the battery module, the fault type and the preliminary cause of the fault at different levels, and can even analyze the specific fault of the battery module reason. Therefore, the user does not need to return the battery module to the original factory to know the various abnormal states of the battery module and the causes of various failures, which is extremely convenient.

The second embodiment of the present invention is a battery diagnosis method, the main flow chart of which is depicted in Fig. 2A. The battery diagnosis method is suitable for an electronic computing device, such as the battery diagnosis device 1 in the first embodiment.

In this embodiment, the electronic computing device stores a fault analysis tree, a plurality of protection parameters of a battery module, and a plurality of internal parameters of the battery module. The battery diagnosis method includes at least step S201 to step S207.

In step S201, the electronic computing device determines a protection state of the battery module according to at least one of the protection parameters (for example, the first diagnosis stage executed by the battery diagnosis device 1). Specifically, the protection parameters include a permanent failure protection parameter, a first safety protection state parameter, and a second safety protection state parameter. In step S201, the electronic computing device determines whether the permanent failsafe state parameter is greater than zero, and if the permanent failsafe state parameter is greater than zero, the electronic computing device determines that the protection state of the battery module is an unrecoverable state.

In step S201, if the electronic computing device determines that the permanent failsafe state parameter is not greater than zero (ie, equal to zero), the electronic computing device further determines whether the first safety protection state parameter is greater than zero. If the electronic computing device determines that the permanent failure protection state parameter is equal to zero and the first safety protection state parameter is greater than zero, the electronic computing device determines the protection state of the battery module It is a first recoverable state. It should be noted that the first recoverable state is related to a type of software/firmware failure.

In step S201, if the electronic computing device determines that the permanent failsafe state parameter is equal to zero and the first safety protection state parameter is not greater than zero (ie, equal to zero), the electronic computing device further determines the second safety protection state parameter Is it greater than zero. If the electronic computing device determines that the permanent failsafe state parameter is equal to zero, the first safety protection state parameter is equal to zero, and the second safety protection state parameter is greater than zero, the electronic computing device determines that the protection state of the battery module is a first 2. Recoverable state. It should be noted that the second recoverable state is related to a type of hardware failure. Next, in step S203, the electronic computing device queries a fault type correspondence table according to the second safety protection state parameter to determine the fault type of the battery module.

In step S203, the electronic computing device determines a fault type of the battery module according to one of the battery protection parameters (for example, the second diagnosis stage executed by the battery diagnosis device 1). If step S201 determines that the protection state of the battery module is an unrecoverable state, then step S203 is that the electronic computing device queries a fault type correspondence table according to the permanent failure protection state parameter to determine the fault type of the battery module. If step S201 determines that the protection state of the battery module is the first recoverable state, then step S203 is that the electronic computing device queries a fault type correspondence table according to the first safety protection state parameter to determine the battery module's protection status. Fault type. If step S201 determines that the protection state of the battery module is the second recoverable state, then step S203 is that the electronic computing device queries a fault type correspondence table according to the second safety protection state parameter to determine the battery module's protection status. Fault type.

In step S205, the electronic computing device queries the fault analysis tree according to the protection status and the fault type to obtain at least one candidate fault cause. Then, in step S207, the electronic computing device determines that a first subset of the internal parameters is consistent with one of the at least one candidate failure cause. According to a judgment rule, it is confirmed that one of the at least one candidate failure cause is a preliminary failure cause of the battery module (for example, the third diagnosis stage executed by the battery diagnosis device 1).

In some embodiments, the battery diagnosis method can execute the flowchart shown in Figure 2B. In these embodiments, the battery diagnosis method further executes step S209 after executing step S207. Specifically, in step S209, the electronic computing device inputs a second subset of the internal parameters into a machine learning model corresponding to the preliminary failure cause to confirm a specific failure cause of the battery module.

It should be noted that, in some embodiments, the fault analysis tree has a first layer, a second layer, a third layer, and a fourth layer. The protection state determined in step S201 corresponds to the first layer, the fault type determined in step S203 corresponds to the second layer, the preliminary failure cause determined in steps S205 to S207 corresponds to the third layer, and The specific failure cause determined in step S209 corresponds to the fourth layer.

In addition to the above steps, the second embodiment can also perform all the operations and steps described in the first embodiment, have the same functions, and achieve the same technical effects. Those with ordinary knowledge in the technical field to which the present invention pertains can directly understand how the second embodiment performs these operations and steps based on the above-mentioned first embodiment, has the same functions, and achieves the same technical effects, so it will not be repeated.

The battery diagnosis method described in the second embodiment can be implemented by a computer program product containing a plurality of program instructions. The computer program product can be a file that can be transmitted over the network, or it can be stored in a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium may be an electronic product, such as: a read only memory (ROM), a flash memory, a floppy disk, a hard disk, and a compact disk (Compact Disk; CD), a digital versatile disc (Digital Versatile Disc; DVD), a flash drive or known to those with ordinary knowledge in the technical field of the present invention And any other storage media with the same function. After the program instructions included in the computer program product are loaded into an electronic computing device (for example, the battery diagnostic device 1), the computer program executes the battery diagnostic method as described in the second embodiment.

It should be clarified that certain terms (including: subset, safety protection state parameters, recoverable state, diagnosis stage, and temperature sensor) in the specification of the present invention patent and the scope of the patent application are preceded by "first" , "Second" and "Third", these "First", "Second" and "Third" are only used to distinguish different terms.

In summary, the battery diagnosis technology (including the device, method and computer program products) provided by the present invention uses a fault analysis tree compiled based on the expert experience of experts in the battery field to perform hierarchical diagnosis. The battery diagnosis technology provided by the present invention analyzes whether the battery module is abnormal according to a plurality of protection parameters of the battery module, and determines which protection state the battery module needs to activate (that is, the unrecoverable state, the first The recoverable state or the second recoverable state), and determine what type of failure the battery module belongs to (that is, the type of hardware failure, the type of software/firmware failure). The battery diagnosis technology provided by the present invention also queries the fault analysis tree according to the protection status and the fault type to obtain at least one candidate fault cause, and determines whether a first subset of the internal parameters meets the candidate fault cause. A corresponding judgment rule is used to confirm that one of the at least one candidate failure cause is a preliminary failure cause of the battery module. In addition, the battery diagnosis technology provided by the present invention can input a second subset of the internal parameters into a machine learning model corresponding to the preliminary failure cause to confirm a specific failure cause of the battery module. Through the foregoing operations, the battery diagnostic technology provided by the present invention performs a hierarchical diagnosis of the battery module-analyzes the protection status, failure type and preliminary failure cause of the battery module at different levels, and even analyzes the battery model The specific failure reason for the group. Therefore, the user does not need to return the battery to the original factory, that is, the user can know the various abnormal status of the battery module and the battery model. The reason for the failure of the group is extremely convenient.

The foregoing embodiments are used to exemplify some implementation aspects of the present invention and explain the technical features of the present invention, and are not used to limit the protection scope and scope of the present invention. Any change or equal arrangement that can be easily completed by a person with ordinary knowledge in the technical field of the present invention belongs to the scope of the present invention, and the protection scope of the present invention is subject to the scope of the patent application.

S201~S209: steps

Claims (13)

A battery diagnostic device, comprising: a memory storing a fault analysis tree, a plurality of protection parameters of a battery module, and a plurality of internal parameters of the battery module; and a processor, which is electrically connected to the memory, Determine a protection status and a fault type of the battery module according to at least one of the protection parameters, query the fault analysis tree according to the protection status and the fault type to obtain at least one candidate fault cause, and determine the internal A first subset of the parameters meets a judgment rule corresponding to one of the at least one candidate failure cause, thereby confirming that one of the at least one candidate failure cause is a preliminary failure cause of the battery module. The battery diagnostic device according to claim 1, wherein the processor also inputs a second subset of the internal parameters into a machine learning model corresponding to the preliminary failure cause to confirm a specific failure cause of the battery module . The battery diagnosis device according to claim 2, wherein the fault analysis tree has a first layer, a second layer, a third layer, and a fourth layer, the protection state corresponds to the first layer, and the fault type Corresponding to the second layer, the preliminary failure reason corresponds to the third layer, and the specific failure reason corresponds to the fourth layer. The battery diagnostic device according to claim 1, wherein the protection parameters include a permanent failure status (Permanent Failure Status) parameter, the processor determines that the permanent failure status parameter is greater than zero, and the processor is based on the permanent failure status The state parameter is greater than zero to determine that the protection state of the battery module is an unrecoverable state, and the processor queries a fault type correspondence table according to the permanent failure protection state parameter to determine the fault type of the battery module. The battery diagnostic device according to claim 1, wherein the protection parameters include a permanent fail-safe state parameter and a first safety protection state parameter, and the processor determines that the permanent fail-safe state parameter is equal to zero and the first safety protection state If the parameter is greater than zero, the processor determines that the protection state of the battery module is a first recoverable state based on that the permanent fail-safe state parameter is equal to zero and the first safety protection state parameter is greater than zero. A software/firmware fault type is related, and the processor queries a fault type correspondence table according to the first safety protection state parameter to determine the fault type of the battery module. The battery diagnostic device according to claim 1, wherein the protection parameters include a permanent fail-safe state parameter, a first safety-protected state parameter, and a second safe-protected state parameter, and the processor determines the permanent fail-safe state parameter Equal to zero, the first safety protection state parameter is equal to zero, and the second safety protection state parameter is greater than zero, the processor is based on the permanent fail protection state parameter being equal to zero, the first safety protection state parameter being equal to zero, and the second safety protection state parameter being greater than Zero determines that the protection state of the battery module is a second recoverable state, and the second recoverable state is related to a hardware failure type. The processor queries a failure type correspondence table according to the second safety protection state parameter To determine the fault type of the battery module. A battery diagnosis method is suitable for an electronic computing device that stores a fault analysis tree, a plurality of protection parameters of a battery module, and a plurality of internal parameters of the battery module. The battery diagnosis method includes the following steps: (a) Determine a protection state of the battery module according to at least one of the protection parameters; (b) Determine a fault type of the battery module according to one of the protection parameters; (c) According to the The protection status and the fault type query the fault analysis tree to obtain at least one candidate fault cause; and (d) Determine that a first subset of the internal parameters meets a judgment rule corresponding to one of the at least one candidate failure cause, thereby confirming that one of the at least one candidate failure cause is a component of the battery module The initial cause of the failure. The battery diagnosis method according to claim 7, further comprising the following steps: input a second subset of the internal parameters into a machine learning model corresponding to the preliminary failure cause to confirm a specific failure cause of the battery module . The battery diagnosis method according to claim 8, wherein the fault analysis tree has a first layer, a second layer, a third layer, and a fourth layer, the protection state corresponds to the first layer, and the fault type Corresponding to the second layer, the preliminary failure reason corresponds to the third layer, and the specific failure reason corresponds to the fourth layer. The battery diagnosis method according to claim 7, wherein the protection parameters include a permanent fail-safe state parameter, and the step (a) is to determine that the permanent fail-safe state parameter is greater than zero, and is based on the permanent fail-safe state parameter greater than zero To determine that the protection state of the battery module is an unrecoverable state, the step (b) is to query a fault type correspondence table according to the permanent failure protection state parameter to determine the fault type of the battery module. The battery diagnosis method according to claim 7, wherein the protection parameters include a permanent fail-safe state parameter and a first safety-protected state parameter, and the step (a) is to determine that the permanent fail-safe state parameter is equal to zero and the first The safety protection state parameter is greater than zero, and based on the permanent fail protection state parameter being equal to zero and the first safety protection state parameter being greater than zero, it is determined that the protection state of the battery module is a first recoverable state, the first recoverable state Related to a software/firmware fault type, the step (b) is to query a fault type correspondence table according to the first safety protection state parameter to determine the fault type of the battery module. The battery diagnosis method according to claim 7, wherein the protection parameters include a permanent failure protection state parameter, a first safety protection state parameter, and a second safety protection state parameter, and the step (a) is to determine the permanent failure The protection state parameter is equal to zero, the first safety protection state parameter is equal to zero and the second safety protection state parameter is greater than zero, and based on the permanent fail protection state parameter being equal to zero, the first safety protection state parameter being equal to zero and the second safety protection state parameter Is greater than zero to determine that the protection state of the battery module is a second recoverable state, and the second recoverable state is related to a hardware failure type, and the step (b) is to query a second safety protection state parameter according to the second recoverable state. The fault type correspondence table is used to determine the fault type of the battery module. A computer program product. After the computer program product is loaded by an electronic computing device, the electronic computing device executes a plurality of program instructions included in the computer program product to perform a battery diagnosis method. The electronic computing device stores a fault analysis Tree, a plurality of protection parameters of a battery module, and a plurality of internal parameters of the battery module, the battery diagnosis method includes the following steps: determining a protection state of the battery module according to at least one of the protection parameters; Determine a fault type of the battery module according to one of the protection parameters; query the fault analysis tree according to the protection status and the fault type to obtain at least one candidate fault cause; and determine a first of the internal parameters The subset meets a judgment rule corresponding to one of the at least one candidate failure cause, thereby confirming that one of the at least one candidate failure cause is a preliminary failure cause of the battery module.

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