Fault detection method and fault detection system applied to battery cabinet

Abstract

The invention discloses a fault detection method applied to a battery cabinet. The fault detection method comprises the following steps: continuously calculating voltage difference data at a calculation frequency; calculating a standard deviation using the current voltage difference data of each battery cabinet; obtaining a first voltage trend according to the voltage difference data in the first period and obtaining a second voltage trend according to the voltage difference data in the second period when the standard deviation is judged to be greater than the preliminary screening threshold value; calculating an intersection of the first voltage trend and the second voltage trend to obtain a voltage trend state; calculating a voltage slope according to the voltage difference data of the second period; and generating warning information when the voltage trend state is an abnormal state and the voltage slope is greater than a slope threshold value, wherein the warning information indicates the position of the battery cell in the overvoltage state.

Description

Technical Field

[0001] This case relates to a detection method and a detection system for a battery cabinet, and particularly relates to a fault detection method and a fault detection system for detecting an abnormal state of a battery cabinet. Background Art

[0002] During the operation of a battery during charging or discharging, the performance of the battery may decline due to overvoltage or overheating conditions, and even battery failure may occur due to overvoltage or overheating, resulting in the malfunction of the device.

[0003] The current monitoring mechanisms provided for the operation of batteries mostly monitor state characteristics such as voltage, current, or temperature to evaluate whether the battery has an abnormal condition. However, the existing methods have some defects: when only using a single state characteristic as the judgment of whether there is an abnormality, misjudgment may occur because other state characteristics of the battery cannot be considered.

[0004] On the other hand, the temperature characteristic of the battery is also one of the factors used to evaluate the state, such as the maximum or minimum value of the temperature. In fact, the battery will present corresponding states as the device operates or idles, etc. For example, when the device is in an idle state, the temperature of the battery will decrease. Therefore, if only the rise or fall of the temperature is considered to evaluate the health state of the battery, the detection result may be incorrect due to the lack of consideration of the operation of the device. Summary of the Invention

[0005] An embodiment of this case provides a fault detection method for a battery cabinet, which is executed in a battery cabinet fault detection system including a voltage data acquisition module and a microcontroller. The battery cabinet includes a plurality of battery modules. The fault detection method includes the following steps: (a) sensing the voltage data of the battery cores of each battery module through the voltage data acquisition module and continuously calculating the voltage difference data of the voltage data by the microcontroller at a calculation frequency; (b) calculating the standard deviation by the microcontroller using the voltage difference data of the battery cabinet so far; (c) when it is determined that the standard deviation is greater than the initial screening threshold value, obtaining a first voltage trend based on the voltage difference data within the first period and obtaining a second voltage trend based on the voltage difference data within the second period by the microcontroller, where the length of the second period is greater than the length of the first period; (d) calculating the intersection of the first voltage trend and the second voltage trend by the microcontroller to obtain a voltage trend state; (e) calculating the voltage slope by the microcontroller according to the voltage difference data of the second period; and (f) when the voltage trend state is in an abnormal state and the voltage slope is greater than the slope threshold value, generating a warning message by the microcontroller, where the warning message indicates that the position of the battery core corresponding to the voltage difference data when the standard deviation is greater than the initial screening threshold value has an overvoltage state.

[0006] Another embodiment of the present case provides a fault detection method for a battery cabinet, which is executed in a battery cabinet fault detection system including a temperature data acquisition module and a microcontroller. The battery cabinet includes a plurality of battery modules. The fault detection method includes the following steps: (a) sensing the temperature data of the battery cabinet through the temperature data acquisition module and calculating the temperature slope, the maximum temperature value and the minimum temperature value among a plurality of temperature characteristic information by using the temperature data through the microcontroller; (b) continuously calculating the temperature difference data of the temperature data at a certain frequency through the microcontroller and calculating the Z-score by using the temperature difference data; (c) setting a first discrete score for the temperature slope, a second discrete score for the maximum temperature value, a third discrete score for the minimum temperature value and a fourth discrete score for the Z-score through the microcontroller; (d) adding up the first discrete score, the second discrete score, the third discrete score and the fourth discrete score through the microcontroller to obtain a score value; and (e) evaluating whether the battery cabinet is in an abnormal state according to the score value through the microcontroller, and generating a warning message when it is evaluated that the battery cabinet is in an abnormal state, wherein the warning message indicates that the position of the maximum temperature value or the minimum temperature value is the battery cabinet where the abnormality occurs. Description of the Drawings

[0007] Figure 1 FIG. is a block diagram of a battery cabinet fault detection system for monitoring the abnormal state of a cabinet by detecting voltage according to an embodiment of the present case;

[0008] Figure 2 FIG. is a flowchart of a fault detection method for a battery cabinet for monitoring the abnormal state of a cabinet by detecting voltage according to an embodiment of the present case;

[0009] Figure 3 FIG. is a block diagram of a battery cabinet fault detection system for monitoring the abnormal state of a cabinet by detecting temperature according to an embodiment of the present case;

[0010] Figure 4 FIG. is a flowchart of a fault detection method for a battery cabinet for monitoring the abnormal state of a cabinet by detecting temperature according to an embodiment of the present case;

[0011] Figure 5 FIG. is a block diagram of a battery cabinet fault detection system for monitoring the abnormal state of a cabinet by detecting voltage according to another embodiment of the present case;

[0012] Figure 6 FIG. is a block diagram of a battery cabinet fault detection system for monitoring the abnormal state of a cabinet by simultaneously detecting voltage and temperature according to another embodiment of the present case.

[0013] Description of the Reference Numerals in the Drawings 10, 20, 30, 40: Battery Cabinet Fault Detection System

[0014] 100, 400: Battery Cabinet

[0015] 110, 410: Battery module

[0016] 120, 420: battery cell

[0017] 210, 510, 610: Voltage data acquisition module

[0018] 220, 320, 520, 620: microcontroller

[0019] 330, 630: Temperature data acquisition module

[0020] S205~S250, S405~S445: Steps DETAILED DESCRIPTION

[0021] The present invention will be further described below in conjunction with the accompanying drawings and embodiments so that relevant persons in the technical field to which the present invention belongs can better understand the present invention and implement it accordingly, but the embodiments are not intended to limit the present invention.

[0022] The fault detection system and method used in the battery cabinet of this case is to monitor the short-term voltage change trend and long-term voltage change trend of the battery cabinet, refer to the voltage change trend of two different cycle time lengths, and use the intersection of these trends to double confirm the increase and decrease state of voltage change. In addition, this case will perform regression operation on the voltage data obtained by detection, and further obtain the slope of voltage change in the corresponding time segment. In this way, this case combines the voltage change trend and the slope of voltage change to comprehensively judge whether the battery cabinet has the precursor of overvoltage, and can know in advance that the battery cabinet is about to have overvoltage problem.

[0023] The fault detection system and method used in the battery cabinet of this case also monitors the change of the temperature difference of the battery cabinet. Specifically, this case uses the time window as a data group to calculate the high temperature change trend and low temperature change trend of the battery cabinet, and combines the maximum temperature value and the minimum temperature value per unit time (per minute), and comprehensively analyzes the above data to diagnose whether there is a precursor to abnormal working function, so as to know the abnormal working phenomenon of the battery cabinet in advance.

[0024] Figure 1 The block diagram of a battery cabinet fault detection system for monitoring cabinet abnormality by detecting voltage according to an embodiment of the present invention.

[0025] At Figure 1 In the embodiment of the present invention, the battery cabinet 100 includes a plurality of battery modules 110 , and each battery module 110 includes a plurality of battery cells 120 .

[0026] The battery cabinet fault detection system 10 includes a voltage data acquisition module 210 and a microcontroller 220. The voltage data acquisition module 210 is coupled to the microcontroller 220.

[0027] The voltage data acquisition module 210 is configured to sense the voltage data of each battery cell 120 of each battery module 110. The battery cabinet fault detection system 10 includes a storage medium (not shown in the figure) for storing the voltage data detected from each battery cell 200. The voltage data is, for example, a time-voltage curve. Since the voltage data acquisition module 210 can identify each battery cell 120, when the microcontroller 220 subsequently detects an abnormal voltage, the position of the battery cell 120 where the abnormality occurs can be traced.

[0028] Figure 2 The flowchart shows a method for detecting faults in a battery cabinet by detecting voltage according to an embodiment of the present case. The following description is to be referred to together with Figure 1 and Figure 2 .

[0029] In step S205, the voltage data acquisition module 210 continuously senses the voltage data of the battery cells 120 of each battery module.

[0030] In step S210, the microcontroller 220 continuously calculates the voltage difference data of each battery cell 120 based on the voltage data of each battery cell 120 at a calculation frequency. For the sake of illustration, the voltage difference data of a single battery cell 120 is used for the following description, and the microcontroller 220 performs the same detection means on each battery cell 120 in the battery cabinet 100.

[0031] In step S215, the microcontroller 220 uses the voltage difference data of the battery cabinet 100 so far to calculate the standard deviation σ.

[0032] In step S220, the microcontroller 220 determines whether the standard deviation is greater than a preliminary threshold value. If the standard deviation is greater than the preliminary threshold value, it means that the voltage difference data so far has changed too much. Therefore, the microcontroller 220 preliminarily determines that there is a possibility of voltage abnormality in the battery cabinet 100 and executes step S225. If the standard deviation is equal to or less than the preliminary threshold value, the microcontroller 220 enters step S250 to determine that the battery cabinet 100 is currently in a normal state.

[0033] In step S225, the microcontroller 220 obtains a first voltage difference trend based on the voltage difference data in each first period and obtains a second voltage difference trend based on the voltage difference data in each second period.

[0034] In step S230, the microcontroller 220 calculates the intersection of the first voltage trend and the second voltage trend to obtain the voltage trend state.

[0035] In step S235, the microcontroller 220 uses the voltage difference data of the second period to calculate the voltage slope.

[0036] In step S240, the microcontroller 220 determines whether the voltage trend state conforms to the potential abnormal state and whether the voltage slope is greater than the slope threshold value. If the determination result is yes, the microcontroller 220 executes step S245. If the determination result is no, the microcontroller 220 enters step S250 to determine that there is no abnormal state currently.

[0037] In step S245, the microcontroller 220 generates a warning message. Since the warning data carries the information of the position of the battery cell corresponding to the voltage difference data when the standard deviation is greater than the preliminary screening threshold value, the user can know which battery cell has overvoltage through the warning message.

[0038] The details of each step are described in detail below.

[0039] In step S205, the voltage data acquisition module 210 continuously obtains the voltage data of each battery cell 120 in the battery cabinet 100, and the microcontroller 220 performs pre-data processing on the voltage data, such as performing a missing value interpolation program.

[0040] In step S210, the microcontroller 220 continuously calculates the voltage difference data of two adjacent voltage data in time at a calculation frequency. In an embodiment, since the voltage data acquisition module 210 continuously obtains the voltage data of each battery cell 120 in the battery cabinet 100, the microcontroller 220 calculates the voltage difference data ΔV between two voltage data at intervals of one minute. Since the voltage data acquisition module 210 continuously acquires voltage data, the voltage difference data ΔV in step S210 is also continuously calculated.

[0041] In step S215, the microcontroller 220 calculates the standard deviation σ of all the voltage difference data ΔV calculated in step S210. In this embodiment, the standard deviation σ can be a value updated every minute.

[0042] In an embodiment, the voltage data acquisition module 210 continuously obtains the voltage data of each battery cell 120 in the battery cabinet 100. The microcontroller 220 calculates the voltage difference data of two adjacent voltage data in time for the voltage data of each battery cell 120 at a calculation frequency (such as every minute), and calculates the standard deviation σ using all the instantaneously generated voltage difference data as samples based on the temporal continuity.

[0043] In step S220, the microcontroller 220 compares the value of the standard deviation σ with the preliminary screening threshold value to preliminarily evaluate the abnormal condition of the battery cabinet 100. For example, when the standard deviation σ is greater than the preliminary screening threshold value, the microcontroller 220 preliminarily determines that there is a potential abnormal condition in the battery cabinet 100, but does not immediately issue any warning information. Instead, it further evaluates the authenticity of the abnormal condition through the long-term voltage trend and the short-term voltage trend.

[0044] In step S225, the microcontroller 220 calculates the first voltage trend (also known as the short-term trend) based on the voltage difference data ΔV within each first cycle. Taking the first cycle as 2 hours as an example. The microcontroller 220 calculates the difference (the first trend data) of the voltage difference data ΔV between the current time and the time 2 hours before the current time at a calculation frequency of per minute. For example, the microcontroller 220 calculates the difference obtained by subtracting the voltage difference data ΔV at 10:00 am from the voltage difference data ΔV at 8:00 am, calculates the difference obtained by subtracting the voltage difference data ΔV at 10:01 am from the voltage difference data ΔV at 8:01 am, and so on. According to the above rules, the microcontroller 220 calculates all the differences within the first cycle as multiple first trend data, and the multiple first trend data constitute the first voltage trend.

[0045] The first trend data can be expressed as follows: ΔV Last round difference =ΔV current -ΔV Last round . Where ΔV current is the voltage difference data at the time of the current cycle, ΔV Last round is the voltage difference data at the time of the previous cycle, and ΔV Last round difference is the difference of the voltage difference data

[0046] Similarly, the microcontroller 220 calculates the second voltage trend (also known as the long-term trend) based on the voltage difference data ΔV within each second cycle. Taking the second cycle as 24 hours (1 day) as an example. The microcontroller 220 calculates the difference (the second trend data) of the voltage difference data ΔV between the current time and the time 24 hours before the current time at a calculation frequency of per minute. For example, the microcontroller 220 calculates the difference obtained by subtracting the voltage difference data ΔV at 8:00 am on February 2 from the voltage difference data ΔV at 8:00 am on February 1, calculates the difference obtained by subtracting the voltage difference data ΔV at 8:01 am on February 2 from the voltage difference data ΔV at 8:01 am on February 1, and so on. According to the above rules, the microcontroller 220 calculates all the differences within the second cycle as multiple second trend data, and the multiple second trend data constitute the second voltage trend.

[0047] The second trend data can be expressed as follows: ΔV Last day difference =ΔVcurrent -ΔV Last day where ΔV current is the voltage difference data of the current cycle time, and ΔV Last day is the voltage difference data of the previous cycle time, and ΔV Last day difference is the difference of the voltage difference data.

[0048] In one embodiment, the length of the second cycle is greater than the length of the first cycle.

[0049] In one embodiment, the ratio of the length of the first cycle to the length of the second cycle is 1:X, where the variable X is a positive integer equal to or greater than 12.

[0050] In step S230, the microcontroller 220 calculates the intersection of the first voltage trend and the second voltage trend in each preset interval to obtain the voltage trend state. In one embodiment, the microcontroller 220 calculates the first number of multiple first trend data greater than the multiple Y of the standard deviation and the second number of multiple first trend data less than or equal to the multiple Y of the standard deviation in the preset interval, and determines the first voltage trend according to the first number and the second number. The multiple Y can be a positive integer equal to or greater than 1 (for example, the multiple Y can be 3). For the convenience of description, the standard deviation is taken as the comparison basis with the first trend data below.

[0051] Taking the preset interval as 2 hours and the calculation frequency as 1 minute as an example. In every two hours, there is 1 first trend data per minute, so there are 120 first trend data in 2 hours. The microcontroller 220 compares these 120 first trend data in this preset interval with the standard deviation respectively, and counts the first number of first trend data greater than the standard deviation (for example, 58) and the second number of first trend data less than or equal to the standard deviation (for example, 62). Then, the microcontroller 220 compares the magnitudes of the first number and the second number. When the first number is greater than the second number, it represents that the voltage change trend based on the first cycle presents a scattered state, and the microcontroller 220 can further determine that there is a voltage abnormal state and determine the first voltage trend as a positive trend. When the first number is less than or equal to the second number, it represents that the voltage change trend based on the first cycle presents a concentrated and stable state, and the microcontroller 220 further determines that there is no voltage abnormal state and determines the first voltage trend as a stable trend.

[0052] Similarly, the microcontroller 220 calculates a third number of multiple second trend data greater than the multiple Y of the standard deviation and a fourth number of multiple second trend data less than or equal to the multiple Y of the standard deviation in each preset interval, and determines the second voltage trend based on the third number and the fourth number. The multiple Y can be a positive integer equal to or greater than 1 (for example, the multiple Y can be 3). For ease of explanation, the standard deviation is taken as the comparison basis for the second trend data below.

[0053] Taking the preset interval as 2 hours and the calculation frequency as 1 minute as an example. In every two hours, there is 1 second trend data per minute, so there are 120 second trend data in 2 hours. The microcontroller 220 compares these 120 second trend data with the standard deviation respectively, and counts the second trend data greater than the standard deviation as the third number (for example, 21) and the second trend data less than or equal to the standard deviation as the fourth number (for example, 99). Then, the microcontroller 220 compares the magnitudes of the third number and the fourth number. When the third number is greater than the fourth number, it means that the voltage change trend based on the second cycle is in a dispersed state. The microcontroller 220 can further determine that there is a voltage abnormal state and determine that the second voltage trend is a positive trend. When the third number is less than or equal to the fourth number, it means that the voltage change trend based on the second cycle is in a concentrated and stable state. The microcontroller 220 further determines that there is no voltage abnormal state and determines that the second voltage trend is a stable trend.

[0054] In an embodiment, the above standard deviation can be calculated based on all voltage difference data of all battery cells (the trend data of each battery cell is compared with the same standard deviation), or calculated separately based on all voltage difference data of each battery cell (each battery cell has its own standard deviation and the trend data of each battery cell is compared with its own standard deviation respectively).

[0055] Please refer back to step S230. The microcontroller 220 calculates the intersection of the first voltage trend and the second voltage trend in each preset interval to obtain the voltage trend state. In one embodiment, the microcontroller 220 performs an intersection of the determination results of the foregoing first voltage trend (belonging to the short-term trend) and the second voltage trend (belonging to the long-term trend), and can obtain the evaluation data of the voltage trend changes with different cycle lengths in each preset interval. In one embodiment, when the first voltage trend is a positive trend and the second voltage trend is a positive trend, the intersection of the two is a positive trend. Therefore, the microcontroller 220 will obtain an evaluation result that the final trend in a preset interval is a positive trend (there is a potential abnormal state). In another embodiment, when one of the first voltage trend and the second voltage trend is a stable trend, the microcontroller 220 will obtain an evaluation result that the final trend in a preset interval is a stable trend. The microcontroller 220 calculates the intersection of the first voltage trend and the second voltage trend in each preset interval. Accordingly, the microcontroller 220 will obtain the voltage trend state (such as a positive trend or a stable trend state) in each preset interval (for example, every two hours). For example, the microcontroller 220 can make an evaluation result of the voltage trend state being a positive trend or a stable trend every two hours.

[0056] In step S210, the microcontroller 220 obtains the distribution data of the time and voltage difference data of each battery cell 120. In one embodiment, the microcontroller 220 finds the most fitting polynomial regression equation from the actual distribution data of these time and voltage difference data, and uses this polynomial regression equation to calculate the voltage slope of the voltage difference data of each second cycle. For example, the microcontroller 220 finds the most fitting binomial equation Y = ax 2 + bx + c, where a, b, and c are constants, x is time, Y is the voltage difference data, and then calculates the voltage slope based on the difference in voltage difference data and the time difference every 2 hours.

[0057] The voltage slope can be expressed as follows: Slope rate = (ΔV Last -ΔV First ) / (ΔT End -ΔT Start ), where ΔV First and ΔT Start are the starting voltage difference data and starting time of the second cycle, and ΔV Last and ΔT End are the ending voltage difference data and ending time of the second cycle.

[0058] In step S240, when the microcontroller 220 simultaneously determines that the voltage trend state is a positive trend and meets the potential abnormal state, and the voltage slope is greater than the slope threshold value, it is determined that it is indeed an abnormal state and a warning message is generated (step S245). Otherwise, the microcontroller 220 determines that there is no abnormal state (step S250).

[0059] In one embodiment, the warning message is used to indicate that an overvoltage state has occurred at the position of the battery cell corresponding to the voltage difference data when the standard deviation is greater than the initial screening threshold value.

[0060] Figure 3 FIG. is a block diagram of a battery cabinet fault detection system for monitoring the abnormal state of a cabinet by detecting temperature according to an embodiment of the present case.

[0061] In Figure 3 the embodiment, the battery cabinet 400 includes a plurality of battery modules 410, and each battery module 410 includes a plurality of battery cells 420. In this embodiment, the battery cabinet fault detection system 20 is used to detect the temperature of the battery cabinet 400 to determine whether the battery cabinet 400 is in an abnormal state.

[0062] The battery cabinet fault detection system 20 includes a temperature data acquisition module 330 and a microcontroller 320. The temperature data acquisition module 330 is coupled to the microcontroller 320.

[0063] In one embodiment, the temperature data acquisition module 330 is disposed in a battery cabinet 400 and is configured to sense the temperature data of the battery cabinet 400 where it is located.

[0064] The battery cabinet fault detection system 20 includes a storage medium (not shown in the figure) for storing the temperature data detected from the battery cabinet 400. The temperature data is, for example, a time-temperature curve. Since the temperature data acquisition module 330 can identify the position of the battery cabinet 400, when the microcontroller 320 subsequently detects that the temperature is abnormal, the position of the battery cabinet 400 where the abnormality occurs can be traced back.

[0065] Figure 4 FIG. is a flowchart of a fault detection method for a battery cabinet implemented by detecting temperature according to an embodiment of the present case. The following description is also referred to Figure 3 and Figure 4 .

[0066] In step S405, the temperature data acquisition module 330 continuously senses the temperature data of the battery cabinet 400.

[0067] In step S410, the microcontroller 320 uses the temperature data to calculate the temperature slope and the maximum temperature value and the minimum temperature value among a plurality of temperature characteristic information.

[0068] In step S415, the microcontroller 320 continuously uses the temperature data at a calculation frequency to calculate the temperature difference data of the battery cabinet 400.

[0069] In step S420, the microcontroller 320 uses the temperature difference data to calculate the Z-score of the battery cabinet 400.

[0070] In step S425, the microcontroller 320 respectively sets a first discrete level for the temperature slope, a second discrete level for the maximum temperature value, a third discrete level for the minimum temperature value, and a fourth discrete level for the Z-score.

[0071] In step S430, the microcontroller 320 sums up the first discrete level, the second discrete level, the third discrete level, and the fourth discrete level to obtain the scoring value of the battery cabinet 400.

[0072] In step S435, the microcontroller 320 determines whether the scoring value is greater than an abnormal threshold value. If the scoring value is greater than the abnormal threshold value, the microcontroller 320 executes step S440. If the scoring value is less than or equal to the abnormal threshold value, the microcontroller 320 enters step S445 to determine that there is no abnormal state currently and continues to monitor.

[0073] In step S440, the microprocessor 320 generates a warning message. Since the warning message indicates the position of the maximum temperature value or the minimum temperature value in the temperature characteristic information, representing the abnormal battery cabinet 400, the user can know which battery cabinet 400 has an abnormality through the warning message.

[0074] The details of each step are described in detail below.

[0075] In an embodiment, the microcontroller 320 uses all the temperature data of the battery cabinet 400 as a calculation group to calculate the temperature slope of the temperature data, extract the maximum temperature value and the minimum temperature value, calculate the temperature difference data, and use the temperature difference data to calculate the Z-score of each battery cabinet 400, which is described in detail as follows.

[0076] In step S405, the temperature data acquisition module 330 continuously senses the temperature data, and these temperature data represent the temperature of the battery cabinet 400 where the temperature data acquisition module 330 is located. The microcontroller 320 performs data preprocessing on the temperature data, such as performing a missing value interpolation program.

[0077] In step S410, the microcontroller 320 uses the temperature data to calculate the temperature slope of the temperature data of the battery cabinet 400. In an embodiment, the temperature data is a time-temperature curve.

[0078] In one embodiment, the temperature slope includes a maximum temperature slope and a minimum temperature slope. The microcontroller 320 extracts a segment of temperature data within a time window and obtains the maximum temperature value and the minimum temperature value in this segment of temperature data. The microcontroller 320 slides this time window to obtain the maximum window temperature value T max and the minimum window temperature value T min for each of the multiple time windows. For the sake of illustration, the maximum window temperature value of the i-th time window is denoted as T max(i) and the minimum window temperature value is denoted as T min(i) . The microcontroller 320 uses the maximum window temperature values T max(i+1) and T max(i) of two adjacent windows to calculate the maximum temperature slope, and uses the minimum window temperature values T min(i+1) and T min(i) of two adjacent windows to calculate the minimum temperature slope.

[0079] In one embodiment, adjacent time windows partially overlap each other in time. For example, the length of a time window is 8 minutes, and two adjacent time windows overlap by 4 minutes. If 8:00 is the starting point, the range of the first time window is from 8:00 to 8:07, and the range of the second time window is from 8:04 to 8:11. The microcontroller 320 respectively obtains the maximum window temperature value T max(1) and the minimum window temperature value T min(1) from 8:00 to 8:07 (the first time window), and the maximum window temperature value T max(2) and the minimum window temperature value T min(2) from 8:04 to 8:11 (the second time window). It is worth mentioning that the degree of overlap between the time windows in this case is not limited. In this embodiment, the example of half the length of the overlapping time windows is used for illustration.

[0080] In one embodiment, the microcontroller 320 calculates the maximum temperature slope based on the maximum window temperature values in two adjacent time windows. The maximum temperature slope can be expressed as follows: where k is the length (in minutes) of a time window. For example, the microcontroller 320 obtains the maximum window temperature value T max(1) from 8:00 to 8:07 and the maximum window temperature value T max(2) from 8:04 to 8:11, calculates the difference T max(2) -T max(1) between the maximum window temperature values of two adjacent time windows, and divides the difference by the time window length (taking 8 minutes as an example) to obtain a value, which is used as the maximum temperature slope for each time window, expressed as follows:

[0081] Similarly, the microcontroller 320 calculates the minimum temperature slope based on the minimum window temperature values in two adjacent time windows. The minimum temperature slope can be expressed as follows: where k is the length (in minutes) of one time window. For example, the microcontroller 320 obtains the minimum window temperature value T min(1) from 8:00 to 8:07 and the minimum window temperature value T min(2) from 8:04 to 8:11, calculates the difference T min(2) -T min(1) between the minimum window temperature values in two adjacent time windows, and divides the difference by the time window length (taking 8 minutes as an example) to obtain the minimum temperature slope for each window, which is expressed as follows:

[0082] In step S410, the microcontroller 320 obtains the maximum temperature value and the minimum temperature value per minute from multiple temperature characteristic information. For example, the microcontroller 320 records the maximum temperature value maxT and the minimum temperature value minT within the one minute from 8:00:00 to 8:00:59. And so on, under continuous monitoring, the microcontroller 320 will obtain multiple maximum temperature values maxT and minimum temperature values minT.

[0083] In step S415, the microcontroller 320 continuously calculates the temperature difference data between two adjacent times at a calculation frequency. In one embodiment, the microcontroller 320 calculates the temperature difference data ΔT between two temperature data at intervals of one minute. For example, the microcontroller 320 subtracts the temperature data at 8:00 from the temperature data at 8:01 to obtain the temperature difference data ΔT representing 8:01. Since the temperature data acquisition module 330 continuously acquires temperature data, the temperature difference data ΔT in step S415 is also continuously generated.

[0084] In step S420, the microcontroller 320 uses the temperature difference data ΔT to calculate the Z-score. In one embodiment, the Z-score can be expressed as: where x is the current temperature difference data ΔT, u is the average value of all temperature difference data ΔT so far, and σ is the standard deviation of all temperature difference data ΔT so far.

[0085] In step S425, the microcontroller 320 sets the first discrete score based on the temperature slope obtained in step S410. Considering multiple adjacent time windows, the microcontroller 320 continuously calculates the maximum temperature slope and the minimum temperature slope in groups of two in multiple time windows. For the sake of illustration, the i-th maximum temperature slope is denoted as S max(i) (the slope obtained from the maximum window temperature values of the (i + 1)-th window and the i-th time window) and the i-th minimum temperature slope is denoted as S min(i)(The slope obtained from the minimum window temperature values of the (i + 1)-th window and the i-th time window).

[0086] In one embodiment, a plurality of consecutive time windows include a first window, a second window, and a third window. The microcontroller 320 calculates a first maximum temperature slope S max(1) and a first minimum temperature slope S min(1) respectively using the maximum window temperature value and the minimum window temperature value of the first window and the second window, and calculates a second maximum temperature slope S max(2) and a second minimum temperature slope S min(2) .

[0087] In one embodiment, the microcontroller 320 determines which slope condition the temperature slope belongs to according to the magnitude relationship of the first maximum temperature slope S max(1) , the first minimum temperature slope S min(1) , the second maximum temperature slope S max(2) , and the second minimum temperature slope S min(2) . The slope conditions can be represented as shown in Table 1.

[0088] Table 1:

[0089] Judgment content Slope condition <![CDATA[S max(1) <S max(2) &S min(1) <S min(2) > First condition <![CDATA[S max(1) >S max(2) &S min(1) <S min(2) > Second condition <![CDATA[S max(1) <S max(2) &S min(1) >S min(2) > Third condition <![CDATA[S max(1) >S max(2) &S min(1) >S min(2) > Fourth condition

[0090] As shown in Table 1, if the first maximum temperature slope S max(1) is less than the second maximum temperature slope S max(2) and the first minimum temperature slope S min(1) is less than the second minimum temperature slope S min(2) , it represents that the temperature change is: the high-temperature part shows an upward trend and the low-temperature part also shows an upward trend (the overall temperature shows an upward trend). At this time, the slope condition belongs to the first condition; if the first maximum temperature slope S max(1) is greater than the second maximum temperature slope S max(2) and the first minimum temperature slope S min(1) is less than the second minimum temperature slope S min(2) , it represents that the temperature change is: the low-temperature part shows an upward trend. At this time, the slope condition belongs to the second condition; if the first maximum temperature slope S max(1) is less than the second maximum temperature slope S max(2) and the first minimum temperature slope S min(1) is greater than the second minimum temperature slope S min(2) , it represents that the temperature change is: the high-temperature part shows an upward trend. At this time, the slope condition belongs to the third condition; if the first maximum temperature slope S max(1) is greater than the second maximum temperature slope S max(2) and the first minimum temperature slope S min(1)Greater than the second minimum temperature slope S min(2) , it represents that the temperature change is as follows: both the high-temperature and low-temperature parts show a downward trend (the overall temperature shows a downward trend). At this time, the slope condition belongs to the fourth condition.

[0091] In an embodiment, the microcontroller 320 gives the first discrete fraction of the temperature slope according to the determined slope condition in cooperation with the slope difference S diff as shown in Table 2.

[0092] Table 2:

[0093]

[0094] In an embodiment, after the microcontroller 320 calculates the maximum temperature slope and the minimum temperature slope of two adjacent time windows (step S410), it will further calculate the difference between the maximum temperature slope and the minimum temperature slope to obtain the slope difference between the two time windows. The slope difference is expressed as follows: S diff = S max(i) - S min(i) . For example, the microcontroller 320 takes the difference obtained by subtracting the minimum temperature slope S max(1) from the maximum temperature slope S min(1) as the slope difference S diff(1) .

[0095] For each slope condition, the microcontroller 320 sets the corresponding value of the first discrete fraction according to the slope difference S diff and the floating threshold value Thr. As shown in Table 2, if under the first condition, the slope difference S diff is greater than the floating threshold value Thr, it represents that the upward trend of the high-temperature part is an abnormal state, so the first discrete fraction is set to 3; conversely, if the slope difference S diff is less than the floating threshold value Thr, it represents that the upward trend of the high-temperature part is a normal state, so the first discrete fraction is set to 0. If under the second condition, the absolute value of the slope difference S diff is greater than the floating threshold value Thr, it represents that the increase amplitude of the low temperature is too large, so the first discrete fraction is set to 3; conversely, it represents that the increase amplitude of the low temperature is within the acceptable range, so the first discrete fraction is set to 0. If under the third condition, the slope difference S diff is greater than several times (for example, 1.5 times) of the floating threshold value, it represents that the upward trend of the high-temperature part exceeds the tolerable amplitude, so the first discrete fraction is set to 3; conversely, it represents that the increase amplitude of the high temperature is within the acceptable range, so the first discrete fraction is set to 0. The fourth condition represents that both the high-temperature and low-temperature parts show a downward trend, representing no temperature increase situation, so the first discrete fraction is set to 0.

[0096] In one embodiment, the floating threshold value is 1.25, but the present case is not limited to this value.

[0097] In step S425, the microcontroller 320 sets a second discrete score for the maximum temperature value obtained in step S410. The setting conditions are shown in Table 3.

[0098] Table 3:

[0099]

[0100] In one embodiment, the microcontroller 320 determines the condition of the maximum temperature value maxT per minute and sets the corresponding second discrete score. In one embodiment, the microcontroller 320 determines whether the critical point of the maximum temperature value maxT is abnormal at 40°C, but the present case is not limited to this value.

[0101] As shown in Table 3, if the maximum temperature value maxT is less than or equal to 40°C, the microcontroller 320 determines that the battery cabinet 400 is in a normal working state, so the second discrete score is set to 0. If the maximum temperature value maxT is greater than 40°C and the current (absolute value) is greater than 1, indicating that the battery cabinet 400 is currently in a working state, the microcontroller 320 determines that the high-temperature situation may have a risk but the risk is not high, so the second discrete score is set to 1. If the maximum temperature value maxT is greater than 40°C and the current (absolute value) is less than or equal to 1, indicating that the battery cabinet 400 is currently in an idle state. Since the temperature value of the battery cabinet 400 in a normal idle state is relatively low (for example, less than 40°C), the microcontroller 320 determines that the current high-temperature situation of the battery cabinet 400 has a risk, so the second discrete score is set to 2.

[0102] In step S425, the microcontroller 320 sets a third discrete score for the minimum temperature value obtained in step S410. The setting conditions are shown in Table 4.

[0103] Table 4:

[0104]

[0105] In one embodiment, the microcontroller 320 determines the condition of the minimum temperature value minT per minute and sets the corresponding third discrete score. In one embodiment, the microcontroller 320 determines whether the critical point of the minimum temperature value minT is abnormal at 10°C, but the present case is not limited to this value.

[0106] As shown in Table 4, if the minimum temperature value minT is greater than 10°C, the microcontroller 320 determines that the battery cabinet 400 is in a normal operating state, and thus sets the third discrete score to 0. If the minimum temperature value minT is less than 10°C and the current (absolute value) is less than 1, indicating that the battery cabinet 400 is currently in an idle state, the microcontroller 320 determines that the low-temperature situation may have a risk but the risk is not high, and thus sets the third discrete score to 1. If the minimum temperature value minT is less than 10°C and the current (absolute value) is greater than or equal to 1, indicating that the battery cabinet 400 is currently in a working state, because the battery cabinet 400 in the working state is in a low-temperature situation (the temperature value of the battery cabinet 400 in the normal working state is relatively high (e.g., greater than 10°C)), the microcontroller 320 determines that the battery cabinet 400 has an abnormal state, and thus sets the third discrete score to 2.

[0107] In step S425, the microcontroller 320 sets a fourth discrete score for the Z-score obtained in step S420. The setting conditions are shown in Table 5.

[0108] Table 5:

[0109]

[0110] In an embodiment, the two critical points of the Z-score are 5 and 10, but the present case is not limited to these values.

[0111] As shown in Table 5, if the Z-score is less than 5, it indicates that the temperature difference of the battery cabinet 400 is within an acceptable range, and thus the microcontroller 320 sets the fourth discrete score to 0. If the Z-score is between 5 and 10, it indicates that the floating degree of the temperature difference of the battery cabinet 400 is relatively large and there may be a risk, and thus the microcontroller 320 sets the fourth discrete score to 1. If the Z-score is greater than 10, it indicates that the temperature difference of the battery cabinet 400 is large and there is a high risk, and thus the microcontroller 320 sets the fourth discrete score to 2.

[0112] In step S430, the microcontroller 320 adds up the set first discrete score, second discrete score, third discrete score, and fourth discrete score to obtain the sum of the four parameter values as the scoring value.

[0113] In step S435, when the scoring value is greater than or equal to the abnormal threshold value, the microcontroller 320 generates a warning message.

[0114] In one embodiment, the anomaly threshold value is the numerical value 6. For example, if the first discrete score is 3 (abnormal high temperature rise in the first condition), the second discrete score is 0 (the maximum temperature value per minute does not exceed 40°C), the third discrete score is 0 (the minimum temperature value per minute is greater than 10°C), and the fourth discrete score is 1 (the situation of temperature fluctuations may be risky), then the sum of the above discrete scores is 4, that is, the scoring value is 4. Since the numerical value 4 does not exceed the anomaly threshold value 6, the microcontroller 320 determines that there is no abnormal state.

[0115] If the first discrete score is 3 (the high temperature rising trend in the third condition exceeds the tolerable range), the second discrete score is 2 (the maximum temperature value per minute exceeds 40°C and the battery cabinet 400 is in an idle state), the third discrete score is 1 (the minimum temperature value per minute is less than 10°C and the battery cabinet 400 is in an idle state), and the fourth discrete score is 0 (the situation of temperature fluctuations is within the acceptable range), then the sum of the above discrete scores is 6, that is, the scoring value is 6. Since the numerical value 6 is equal to the anomaly threshold value 6, the microcontroller 320 determines an abnormal state.

[0116] Since the warning information indicates the position of the maximum temperature value or the minimum temperature value in the temperature characteristic information, and this position represents the battery cabinet 400 where the anomaly occurs, the user can thus know which battery cabinet 400 has an anomaly through the warning information. In the above embodiment, the user can find the corresponding battery cabinet 400 based on the abnormal maximum temperature value maxT, and can take corresponding measures in advance to prevent disasters from occurring.

[0117] In one embodiment, the fault detection method for battery cabinets implemented by detecting temperature proposed in this case is applicable to the application scenarios of one or more battery cabinets, without limitation Figure 3 to the number of battery cabinets 400. For example, when Figure 3 the application scenario is multiple battery cabinets 400, the battery cabinet fault detection system 20 includes multiple temperature data acquisition modules 330. Each temperature data acquisition module 330 is disposed in a battery cabinet 400 and is configured to sense the temperature data of the battery cabinet 400 where it is located. The microcontroller 320 is coupled to all the temperature data acquisition modules 330 and executes the steps of the battery cabinet fault detection for detecting temperature.

[0118] Figure 5 FIG. is a block diagram of a battery cabinet fault detection system for monitoring the abnormal state of the cabinet by detecting voltage according to another embodiment shown in this case.

[0119] In Figure 5In an embodiment, the application scenario considers multiple battery cabinets 400, each battery cabinet 400 includes multiple battery modules 410, and each battery module 410 includes multiple battery cells 420.

[0120] The battery cabinet fault detection system 30 includes multiple voltage data acquisition modules 510 and a microcontroller 520. Each voltage data acquisition module 510 is coupled to the microcontroller 520. In this embodiment, each voltage data acquisition module 510 is disposed in a corresponding battery cabinet 400 and is configured to sense the voltage data of each battery cell 420 of each battery module 410.

[0121] In one embodiment, the battery cabinet fault detection system 30 is configured to perform voltage detection to implement a fault detection method for the battery cabinet. For example, each voltage data acquisition module 510 transmits the sensed voltage data to the microcontroller 520, and the microcontroller 520 executes the Figure 2 fault detection method as described above.

[0122] Figure 6 FIG. is a block diagram of a battery cabinet fault detection system for simultaneously detecting voltage and temperature to monitor the abnormal state of the cabinet according to another embodiment of the present case.

[0123] In Figure 6 an embodiment, the application scenario considers multiple battery cabinets 400, each battery cabinet 400 includes multiple battery modules 410, and each battery module 410 includes multiple battery cells 420.

[0124] The battery cabinet fault detection system 40 includes multiple voltage data acquisition modules 610, a microcontroller 620, and multiple temperature data acquisition modules 630. Each voltage data acquisition module 610 and each temperature data acquisition module 630 are respectively coupled to the microcontroller 620.

[0125] In this embodiment, each voltage data acquisition module 610 is disposed in a corresponding battery cabinet 400 and is configured to sense the voltage data of each battery cell 620 of each battery module 610.

[0126] In this embodiment, each temperature data acquisition module 630 is disposed in a corresponding battery cabinet 400 and is configured to sense the temperature data of each battery cabinet 400.

[0127] In one embodiment, the battery cabinet fault detection system 40 is configured to simultaneously perform voltage and temperature detection to implement a fault detection method for the battery cabinet. For example, each voltage data acquisition module 610 transmits the sensed voltage data to the microcontroller 620, and each temperature data acquisition module 630 transmits the sensed temperature data to the microcontroller 620, and the microcontroller 620 executes theFigure 2 and Figure 4 the described fault detection method.

[0128] In one embodiment, the microcontroller is, for example but not limited to, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Central Processing Unit (CPU), a System on Chip (SoC), a Field Programmable Gate Array (FPGA), a Network Processor chip, or a combination of the above components.

[0129] In one embodiment, the battery cabinet can be an energy storage system, such as a battery station of a vehicle-mounted device or an energy storage system of green energy, etc. The battery cabinet fault detection system and method can be used to detect the above battery cabinet or energy storage system.

[0130] In summary, this case proposes a fault detection method for a battery cabinet by detecting voltage and temperature, and a battery cabinet fault detection system that combines the detection of voltage and temperature. In addition to being able to detect the abnormal state of the battery cabinet earlier and improve the accuracy of determining abnormalities, it can also take corresponding measures for the corresponding battery cabinet based on the location of voltage and / or temperature abnormalities to avoid disasters.

[0131] The above are only specific examples of this case and do not limit the claims of this case. Therefore, all equivalent changes made by using the content of this case are similarly included within the scope of this case, which is hereby stated.

Claims

1. A fault detection method for a battery cabinet, which is executed in a battery cabinet fault detection system including a voltage data acquisition module and a microcontroller, wherein the battery cabinet includes a plurality of battery modules, and the method includes: (a) Sensing voltage data of battery cells of the battery modules through the voltage data acquisition module, and continuously calculating voltage difference data of the voltage data at a calculation frequency through the microcontroller; (b) Calculating a standard deviation by the microcontroller using the voltage difference data of the battery cabinet so far; (c) When it is determined that the standard deviation is greater than a preliminary screening threshold value, obtaining a first voltage trend based on the voltage difference data within a first period and obtaining a second voltage trend based on the voltage difference data within a second period by the microcontroller, wherein the length of the second period is greater than the length of the first period; (d) Calculating an intersection of the first voltage trend and the second voltage trend by the microcontroller to obtain a voltage trend state; (e) Calculating a voltage slope by the microcontroller according to the voltage difference data of the second period; and (f) When the voltage trend state is a potential abnormal state and the voltage slope is greater than a slope threshold value, generating a warning message by the microcontroller, wherein the warning message indicates that an overvoltage state occurs at the position of the battery cell corresponding to the voltage difference data when the standard deviation is greater than the preliminary screening threshold value.

2. The fault detection method according to claim 1, wherein step (c) further includes: (c1) Calculating a difference between the voltage difference data between each of the first periods to obtain a plurality of first trend data; (c2) Calculating a first number of the plurality of first trend data greater than the standard deviation and a second number of the plurality of first trend data less than or equal to the standard deviation; (c3) Judging the first voltage trend according to the first number and the second number; (c4) Calculating a difference between the voltage difference data between each of the second periods to obtain a plurality of second trend data; (c5) Calculating a third number of the plurality of second trend data greater than the standard deviation and a fourth number of the plurality of second trend data less than or equal to the standard deviation; And (c6) Judging the second voltage trend according to the third number and the fourth number.

3. The fault detection method according to claim 2, wherein step (c3) further includes that when the first number is greater than the second number, the microcontroller determines that the first voltage trend is a positive trend, and step (c6) further includes that when the third number is greater than the fourth number, the microcontroller determines that the second voltage trend is the positive trend.

4. The fault detection method according to claim 3, wherein step (d) includes that when the intersection of the first voltage trend and the second voltage trend is the positive trend, the microcontroller indicates that the voltage trend state is the potential abnormal state.

5. The fault detection method according to claim 1 further includes calculating the voltage difference of the battery cells at the calculated frequency per minute by the microcontroller to obtain the voltage difference data.

6. The fault detection method according to claim 1, wherein the battery cabinet fault detection system includes a temperature data acquisition module coupled to the microcontroller, and the fault detection method further includes: (g) Sensing the temperature data of the battery cabinet by the temperature data acquisition module, and calculating the temperature slope and the maximum temperature value and the minimum temperature value among a plurality of temperature characteristic information by the microcontroller using the temperature data; (h) Continuously calculating the temperature difference data of the temperature data at the calculated frequency by the microcontroller and using the temperature difference data to calculate the Z-score; (i) Setting a first discrete score for the temperature slope, a second discrete score for the maximum temperature value, a third discrete score for the minimum temperature value, and a fourth discrete score for the Z-score by the microcontroller; (j) Summing the first discrete score, the second discrete score, the third discrete score, and the fourth discrete score by the microcontroller to obtain a score value; and (k) Evaluating whether the battery cabinet is in an abnormal state by the microcontroller according to the score value, and generating a warning message when it is evaluated that the battery cabinet is in the abnormal state, wherein the warning message indicates that the position of the maximum temperature value or the minimum temperature value is the battery cabinet where the abnormality occurs.

7. The fault detection method according to claim 6, wherein the temperature slope includes a maximum temperature slope and a minimum temperature slope, and the step of calculating the temperature slope in step (g) includes: (g1) Obtaining the maximum window temperature value and the minimum window temperature value of the temperature data in the time window by the microcontroller; (g2) Calculating the maximum temperature slope between the maximum window temperature values of two adjacent time windows; and (g3) Calculating the minimum temperature slope between the minimum window temperature values of two adjacent time windows, wherein two adjacent time windows partially overlap.

8. The fault detection method according to claim 7, wherein the steps after calculating the maximum temperature slope and the minimum temperature slope of two adjacent time windows further include: (g4) Calculating the difference between the maximum temperature slope and the minimum temperature slope by the microcontroller to obtain the slope difference between two adjacent time windows.

9. The fault detection method according to claim 8, wherein step (j) includes calculating a first maximum temperature slope and a first minimum temperature slope and a second maximum temperature slope and a second minimum temperature slope respectively from three adjacent time windows with two consecutive adjacent time windows as a group, and setting the first discrete score by the following plurality of slope conditions, wherein the plurality of slope conditions include: The first maximum temperature slope is less than the second maximum temperature slope, the first minimum temperature slope is less than the second minimum temperature slope, and the slope difference is greater than a floating threshold value; The first maximum temperature slope is greater than the second maximum temperature slope, the first minimum temperature slope is less than the second minimum temperature slope, and the absolute value of the slope difference is greater than the floating threshold value; The first maximum temperature slope is less than the second maximum temperature slope, the first minimum temperature slope is greater than the second minimum temperature slope, and the slope difference is several times greater than the floating threshold value; and The first maximum temperature slope is greater than the second maximum temperature slope and the first minimum temperature slope is greater than the second minimum temperature slope.

10. The fault detection method according to claim 9, wherein step (h) includes: Continuously calculating the difference in temperature between adjacent times at the calculation frequency per minute to obtain the temperature difference data; Continuously calculating an average value and the standard deviation of the temperature difference data up to now; and Calculate the Z-score of the current temperature using the mean value and the standard deviation, where the Z-score is x is the current temperature difference data, u is the mean value of all temperature difference data so far, and σ is the standard deviation of all temperature difference data so far.

11. The fault detection method according to claim 10, wherein step (j) further includes: Continuously detecting the maximum temperature value per minute in the temperature data, and giving a second discrete score according to the maximum temperature value and the charge and discharge state of the current; Continuously detecting the minimum temperature value per minute in the temperature data, and giving a third discrete score according to the minimum temperature value and the charge and discharge state of the current; and Giving a fourth discrete score according to the numerical range of the Z-score.

12. A fault detection method for a battery cabinet, which is executed in a battery cabinet fault detection system including a temperature data acquisition module and a microcontroller, wherein the battery cabinet includes a plurality of battery modules, and the method includes: (a) Sensing the temperature data of the battery cabinet through the temperature data acquisition module, and calculating the temperature slope, the maximum temperature value and the minimum temperature value among a plurality of temperature characteristic information by using the temperature data through the microcontroller; (b) Continuously calculating the temperature difference data of the temperature data at the calculation frequency through the microcontroller and calculating the Z-score by using the temperature difference data; (c) Setting a first discrete score for the temperature slope, a second discrete score for the maximum temperature value, a third discrete score for the minimum temperature value, and a fourth discrete score for the Z-score by the microcontroller; (d) Summing up the first discrete score, the second discrete score, the third discrete score and the fourth discrete score by the microcontroller to obtain a score value; and (e) Evaluating whether the battery cabinet is in an abnormal state according to the score value by the microcontroller, and generating a warning message when it is evaluated that the battery cabinet is in the abnormal state, wherein the warning message indicates that the position of the maximum temperature value or the minimum temperature value is the battery cabinet where the abnormality occurs.

13. The fault detection method according to claim 12, wherein the temperature slope includes a maximum temperature slope and a minimum temperature slope, and the step of calculating the temperature slope in step (a) includes: (a1)Obtain the maximum window temperature value and the minimum window temperature value of the temperature data in the time window through the microcontroller; (a2)Calculate the maximum temperature slope between the maximum window temperature values of two adjacent time windows; and (a3)Calculate the minimum temperature slope between the minimum window temperature values of two adjacent time windows, where the two adjacent time windows partially overlap.

14. The fault detection method according to claim 13, wherein the steps after calculating the maximum temperature slope and the minimum temperature slope of two adjacent time windows further include: (a4)Calculate the difference between the maximum temperature slope and the minimum temperature slope through the microcontroller to obtain the slope difference between two adjacent time windows.

15. The fault detection method according to claim 14, wherein step (c) includes calculating a first maximum temperature slope and a first minimum temperature slope and a second maximum temperature slope and a second minimum temperature slope respectively from three adjacent time windows in groups of two adjacent time windows, and setting the first discrete score through the following multiple slope conditions, where the multiple slope conditions include: The first maximum temperature slope is less than the second maximum temperature slope, the first minimum temperature slope is less than the second minimum temperature slope, and the slope difference is greater than the floating threshold; The first maximum temperature slope is greater than the second maximum temperature slope, the first minimum temperature slope is less than the second minimum temperature slope, and the absolute value of the slope difference is greater than the floating threshold; The first maximum temperature slope is less than the second maximum temperature slope, the first minimum temperature slope is greater than the second minimum temperature slope, and the slope difference is greater than several times the floating threshold; And The first maximum temperature slope is greater than the second maximum temperature slope and the first minimum temperature slope is greater than the second minimum temperature slope.

16. The fault detection method according to claim 15, wherein step (b) includes: Continuously calculate the difference in temperature between adjacent times at the calculation frequency of per minute to obtain the temperature difference data; Continuously calculate the average value and the standard deviation of the temperature difference data so far; And Calculate the Z-score of the current temperature using the mean value and the standard deviation, where the Z-score is x is the current temperature difference data, u is the mean value of all temperature difference data so far, and σ is the standard deviation of all temperature difference data so far.

17. The fault detection method according to claim 16, wherein step (c) further includes: Continuously detect the maximum temperature value per minute in the temperature data, and give the second discrete score according to the maximum temperature value and the charge and discharge state of the current; Continuously detect the minimum temperature value per minute in the temperature data, and give the third discrete score according to the minimum temperature value and the charge and discharge state of the current; and Give the fourth discrete score according to the numerical range of the Z-score.

18. A fault detection system applied to multiple battery cabinets, wherein each of the battery cabinets includes a plurality of battery modules, and the fault detection system includes: A plurality of voltage data acquisition modules, each of the voltage data acquisition modules is configured to sense the voltage data of a plurality of battery cells of each of the battery modules in the battery cabinet where it is located; Multiple temperature data acquisition modules, each of the temperature data acquisition modules being configured to sense the temperature data of the battery cabinet where it is located; And A microcontroller, coupled to each of the voltage data acquisition modules and each of the temperature data acquisition modules, and configured to execute the fault detection method as claimed in claim 6.

Images