Battery pack fan fault detection method and system and storage medium

Abstract

The invention relates to a battery pack fan fault detection method and system and a storage medium. The method comprises the following steps: acquiring characteristic parameter data of each cooling fan in an air-cooled energy storage system; performing curve fitting processing on each characteristic parameter data to obtain a characteristic parameter curve corresponding to each cooling fan; and on the basis of a preset curve model, the characteristic parameter curves are recognized, when the characteristic parameter curve of any cooling fan meets a preset abnormal curve condition, the corresponding cooling fan is determined to be a fault fan, and timely and accurate recognition of the fault fan is achieved. According to the method, the characteristic parameter data of the cooling fan is subjected to curvilinear processing, and the corresponding characteristic parameter curve is identified, so that the working condition of the fan is automatically judged, the working state of the fan is fed back in time, the fan troubleshooting process is simplified, the faulty fan can be found in time, the fan fault identification efficiency is high, the accuracy is high, and the user experience is improved. And normal operation of the air-cooled energy storage system is prevented from being affected by damage of the fan.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of batteries, in particular to a battery pack fan fault detection method, system and storage medium. BACKGROUND

[0002] The air-cooled energy storage system mainly relies on air flow for heat dissipation. The air-cooled energy storage system includes a plurality of battery packs, and each battery pack is provided with a heat dissipation fan. The heat dissipation fan makes the air flow to take away the heat generated by the battery pack, thereby reducing the surface temperature of the battery pack.

[0003] In the existing fan fault detection method of the air-cooled energy storage system, when the air-cooled energy storage system has an abnormal cell temperature difference, the abnormality is checked on site (such as listening to the sound of the fan on site), so as to identify the faulty fan. However, on-site troubleshooting is tedious and has a lag, which makes it impossible to find the faulty fan in time, the efficiency of fan fault identification is low and the lag is large, and the normal operation of the air-cooled energy storage system is affected. SUMMARY

[0004] Therefore, a battery pack fan fault detection method, system and storage medium are provided.

[0005] In a first aspect, the present application provides a battery pack fan fault detection method, comprising the following steps: Obtaining characteristic parameter data of each heat dissipation fan in the air-cooled energy storage system; Performing curve fitting processing on each characteristic parameter data to obtain a characteristic parameter curve corresponding to each heat dissipation fan; Based on a preset curve model, identifying each characteristic parameter curve, and determining that the corresponding heat dissipation fan is a faulty fan when the characteristic parameter curve of any heat dissipation fan meets a preset abnormal curve condition.

[0006] In one of the embodiments, the characteristic parameter data includes electrical parameter data and vibration parameter data. The step of performing curve fitting processing on each characteristic parameter data to obtain a characteristic parameter curve corresponding to each heat dissipation fan comprises: Performing curve fitting processing on each electrical parameter data and each vibration parameter data to obtain an electrical parameter curve corresponding to each heat dissipation fan and a vibration parameter curve corresponding to each heat dissipation fan; The step of determining that the corresponding heat dissipation fan is a faulty fan when the characteristic parameter curve of any heat dissipation fan meets a preset abnormal curve condition comprises: According to the electrical parameter curve and the vibration parameter curve corresponding to the faulty fan, obtaining the fault type of the faulty fan.

[0007] In one of the embodiments, the air-cooled energy storage system comprises a plurality of battery clusters, each of the battery clusters comprising a plurality of battery packs, each of the battery packs comprising at least one cooling fan; each of the battery clusters is provided with cluster number information, each of the battery packs is provided with pack number information, and each of the cooling fans is provided with fan number information; The step of obtaining the fault type of the faulty fan according to the electrical parameter curve and the vibration parameter curve of the faulty fan comprises: The fan number information, the battery pack number information, and the battery cluster number information of the faulty fan are obtained. The positioning information of the faulty fan is obtained according to the corresponding fan number information, the battery pack number information, and the battery cluster number information. The fan icon corresponding to the faulty fan is marked according to the positioning information, and the marked fan icon is displayed.

[0008] In one of the embodiments, the step of marking the fan icon corresponding to the faulty fan according to the positioning information and displaying the marked fan icon comprises: The fan icon corresponding to the faulty fan is color-marked according to the positioning information, and the color-marked fan icon and the fault type are displayed.

[0009] In one of the embodiments, the electrical parameter data comprises current data, voltage data, and power data; the electrical parameter curve comprises a current change curve corresponding to the current data, a voltage change curve corresponding to the voltage data, and a power change curve corresponding to the power data. The step of obtaining the fault type of the faulty fan according to the electrical parameter curve and the vibration parameter curve of the faulty fan comprises: The characteristic values of the current change curve, the voltage change curve, and the power change curve are extracted to obtain current characteristic values, voltage characteristic values, and power characteristic values. When the current characteristic values fall within a first current threshold range, the voltage characteristic values fall within a first voltage threshold range, and the power characteristic values fall within a first power threshold range, it is determined that the fault type of the faulty fan is fan stall. When the current characteristic values fall within a second current threshold range, the voltage characteristic values fall within a second voltage threshold range, and the power characteristic values fall within a second power threshold range, it is determined that the fault type of the faulty fan is fan open circuit.

[0010] In one of the embodiments, the vibration parameter data comprises first direction axis vibration data, second direction axis vibration data, and third direction axis vibration data; the vibration parameter curve comprises a first direction axis vibration curve corresponding to the first direction axis vibration data, a second direction axis vibration curve corresponding to the second direction axis vibration data, and a third direction axis vibration curve corresponding to the third direction axis vibration data. According to the electrical parameter curve and the vibration parameter curve of the corresponding fault fan, the step of obtaining the fault type of the fault fan further comprises: The characteristic values of the first direction axis vibration curve, the second direction axis vibration curve and the third direction axis vibration curve are extracted to obtain first vibration characteristic values, second vibration characteristic values and third vibration characteristic values; When the first vibration characteristic values fall within a first vibration threshold range, the second vibration characteristic values fall within a second vibration threshold range, and the third vibration characteristic values fall within a third vibration threshold range, it is determined that the fault type of the fault fan is fan blade damage.

[0011] In one embodiment, the step of identifying and processing each characteristic parameter curve based on the preset curve model further comprises: According to each characteristic parameter curve, the preset curve model is subjected to machine learning processing to update the preset curve model.

[0012] In a second aspect, the application also provides a battery pack fan fault detection system applied to a forced air cooling energy storage system, the forced air cooling energy storage system comprising a plurality of cooling fans; the battery pack fan fault detection system comprising a processing device and a plurality of acquisition modules, the processing device being connected to each acquisition module; each acquisition module being arranged in one-to-one correspondence with each cooling fan; the acquisition module being used to detect characteristic parameter data of the corresponding cooling fan; The processing device is used to execute the steps of the battery pack fan fault detection method of any one of the above.

[0013] In one embodiment, the acquisition module comprises a current acquisition module, a voltage acquisition module and an acceleration sensing module; The current acquisition module and the voltage acquisition module are connected to the power supply end of the corresponding cooling fan, and the acceleration sensing module is arranged on one side of the corresponding cooling fan; The current acquisition module, the voltage acquisition module and the acceleration sensing module are connected to the processing device.

[0014] In a third aspect, the application also provides a computer storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of the battery pack fan fault detection method of any one of the above.

[0015] One of the above technical solutions has the following advantages and beneficial effects: In the battery pack fan fault detection method described above, the characteristic parameter data of each cooling fan in the air-cooled energy storage system is obtained; the characteristic parameter data is subjected to curve fitting processing to obtain a characteristic parameter curve corresponding to each cooling fan; and based on a preset curve model, the characteristic parameter curves are subjected to identification processing, and when the characteristic parameter curve of any cooling fan meets a preset abnormal curve condition, the corresponding cooling fan is determined as a fault fan, thereby realizing timely and accurate identification of the fault fan. Through curve processing of the characteristic parameter data of the cooling fan and identification of the corresponding characteristic parameter curve, the working condition of the fan is automatically determined, the working state of the fan is fed back in a timely manner, the fan fault troubleshooting process is simplified, the fault fan can be found in a timely manner, the fan fault identification efficiency is high and the accuracy is high, and damage of the fan to the normal operation of the air-cooled energy storage system is avoided. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 An application environment schematic diagram of the battery pack fan fault detection method in the embodiments of the present application is shown in the figure. Figure 2 A first flowchart of the battery pack fan fault detection method in the embodiments of the present application is shown in the figure. Figure 3 A flowchart of the fault positioning display step in the embodiments of the present application is shown in the figure. Figure 4 A first flowchart of the fault type determination step in the embodiments of the present application is shown in the figure. Figure 5 A second flowchart of the fault type determination step in the embodiments of the present application is shown in the figure. Figure 6 A structure connection schematic diagram of the battery pack fan fault detection system in the embodiments of the present application is shown in the figure. Figure 7 A structure schematic diagram of the battery pack fan fault detection system in the embodiments of the present application is shown in the figure. DETAILED DESCRIPTION

[0017] In order to enable personnel in the technical field to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0018] It should be noted that the terms "first", "second" and the like in the description and in the claims of the present application and the above-described accompanying drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0019] In addition, the term "a plurality of" should mean two and more than two.

[0020] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] The battery pack fan fault detection method provided by the present application can be applied to the application environment as shown in Figure 1 The battery pack fan fault detection system includes a processing device 100 and a plurality of acquisition modules 200, the processing device 100 is connected to each acquisition module 200; each acquisition module 200 is set in one-to-one correspondence with each cooling fan 300; the acquisition module 200 is used to detect the characteristic parameter data of the corresponding cooling fan 300. The processing device 100 can include a memory and a processing module, the memory is used to store information such as characteristic parameter data, characteristic parameter curve and preset curve model; the processing module is used to obtain the characteristic parameter data of each cooling fan 300 in the air-cooled energy storage system; the characteristic parameter data is subjected to curve fitting processing to obtain the characteristic parameter curve corresponding to each cooling fan 300; based on the preset curve model, each characteristic parameter curve is subjected to identification processing, and when the characteristic parameter curve of any cooling fan 300 meets the preset abnormal curve condition, the corresponding cooling fan 300 is determined as a fault fan. The processing device 100 further includes a display connected to the processing module, and the display is used to graphically display information such as characteristic parameter curve, preset curve model and fault fan. The battery pack fan fault detection system can be applied to the air-cooled energy storage system, which includes a plurality of battery packs and a plurality of cooling fans 300, each battery pack is provided with at least one cooling fan 300, and the cooling fan 300 is used to cool the battery pack.

[0022] In one embodiment, as shown in Figure 2 A battery pack fan fault detection method is also provided, comprising the following steps: Step S210, obtaining the characteristic parameter data of each cooling fan in the air-cooled energy storage system.

[0023] The air-cooled energy storage system can be an air-cooled lithium battery energy storage system, and the air-cooled energy storage system has a plurality of cooling fans for cooling the corresponding battery pack. For example, the cooling fan can be an axial fan or a centrifugal fan. The type of cooling fan can be determined according to the specific size and power of the battery pack. For example, the battery pack is provided with one or two cooling fans. The cooling fan has an air inlet end and an air outlet end. The air inlet end of the cooling fan is arranged adjacent to one end of the corresponding battery pack to cool the corresponding battery pack by air suction. For example, the air outlet end of the cooling fan is arranged adjacent to one end of the corresponding battery pack to cool the corresponding battery pack by air blowing.

[0024] The characteristic parameter data refers to the characteristic acquisition data of the corresponding cooling fan. For example, the characteristic parameter data can be current, voltage, and power data of the corresponding cooling fan. For example, the characteristic parameter data can also include temperature data of the corresponding cooling fan.

[0025] For example, during the operation of the air-cooled energy storage system, the acquisition module detects the characteristic parameter data of the corresponding cooling fan in real time, and transmits the detected characteristic parameter data to the processing device, and then the processing device obtains the characteristic parameter data of each cooling fan.

[0026] Step S220, curve fitting processing is performed on each characteristic parameter data to obtain a characteristic parameter curve corresponding to each cooling fan.

[0027] The characteristic parameter data includes characteristic acquisition data at different collection time points. By performing curve fitting processing on the corresponding characteristic parameter data, the characteristic parameter curve of the corresponding cooling fan is obtained. It should be noted that the characteristic parameter curve can be obtained by establishing a two-dimensional coordinate system, with time as the horizontal coordinate and the characteristic parameter amplitude as the vertical coordinate.

[0028] Step S230, based on a preset curve model, each characteristic parameter curve is identified, and when the characteristic parameter curve of any cooling fan meets a preset abnormal curve condition, the corresponding cooling fan is determined as a fault fan.

[0029] The preset curve model can be established based on historical test data. For example, test characteristic parameter data in different states of the air-cooled energy storage system can be set. Based on different test characteristic parameter data, test characteristic parameter curve graphs in various working states (including normal working states and fault working states of different fault types) are recorded before the air-cooled energy storage system is debugged (including replacing cooling fans with different degrees of damage to test the change of the curve). Then, according to each working state and each test characteristic parameter curve graph, the preset curve model is established. The preset abnormal curve condition can be preset by the system, for example, the preset abnormal curve condition includes whether the curve characteristic value exceeds the abnormal threshold value, etc.

[0030] By matching each characteristic parameter curve with a preset curve model, fault recognition is performed on each characteristic parameter curve based on a preset abnormal curve condition, and according to the recognition result, when the characteristic parameter curve of any one of the heat dissipation fans meets the preset abnormal curve condition, it is determined that the corresponding heat dissipation fan has a fault, and the corresponding heat dissipation fan is determined as a fault fan, so as to automatically judge the working state of the heat dissipation fan and timely feedback the fault fan detected.

[0031] In the above embodiment, the characteristic parameter data of each heat dissipation fan in the air-cooled energy storage system is obtained, curve fitting processing is performed on each characteristic parameter data to obtain the characteristic parameter curve corresponding to each heat dissipation fan, and based on a preset curve model, each characteristic parameter curve is identified, and when the characteristic parameter curve of any one of the heat dissipation fans meets the preset abnormal curve condition, the corresponding heat dissipation fan is determined as a fault fan, so as to timely and accurately identify the fault fan. The present application realizes automatic determination of the working condition of the fan by performing curve fitting processing on the characteristic parameter data of the heat dissipation fan and identifying the corresponding characteristic parameter curve, timely feedbacks the working state of the fan, simplifies the fan troubleshooting process, can timely find the fault fan, has high efficiency and high accuracy in fan fault identification, and avoids damage of the fan to affect the normal operation of the air-cooled energy storage system.

[0032] In one embodiment, the characteristic parameter data includes electric parameter data and vibration parameter data. The step of performing curve fitting processing on each characteristic parameter data to obtain the characteristic parameter curve corresponding to each heat dissipation fan includes: performing curve fitting processing on each electric parameter data and each vibration parameter data to obtain the electric parameter curve corresponding to each heat dissipation fan and the vibration parameter curve corresponding to each heat dissipation fan.

[0033] The electric parameter data refers to the power supply parameter data of the heat dissipation fan, for example, the electric parameter data can be the working voltage, working voltage and working power of the heat dissipation fan, etc. The vibration parameter data refers to the frequency data or acceleration data generated during the working process of the heat dissipation fan.

[0034] The electric parameter data includes electric parameter collection data at different collection time points; the vibration parameter data includes characteristic parameter collection data at different collection time points. By performing curve fitting processing on the corresponding electric parameter data, the electric parameter curve of the corresponding heat dissipation fan is obtained; by performing curve fitting processing on the corresponding vibration parameter data, the vibration parameter curve of the corresponding heat dissipation fan is obtained. It should be noted that the electric parameter curve can be obtained by establishing a two-dimensional coordinate system, the horizontal coordinate is time, and the vertical coordinate is the electric parameter amplitude; the vibration parameter curve can be obtained by establishing a two-dimensional coordinate system, the horizontal coordinate is time, and the vertical coordinate is the acceleration amplitude in the corresponding direction.

[0035] In one example, after the step of determining the corresponding heat dissipation fan as the fault fan when the characteristic parameter curve of any one heat dissipation fan meets the preset abnormal curve condition, the step includes: According to the electric parameter curve and the vibration parameter curve corresponding to the fault fan, the fault type of the fault fan is obtained.

[0036] For example, by matching each electric parameter curve with a preset curve model, matching each vibration parameter curve with a preset curve model, performing fault identification on each electric parameter curve and each vibration parameter curve based on the preset abnormal curve condition, and according to the identification result, when the electric parameter curve of any one heat dissipation fan meets the preset abnormal curve condition, it is determined that the corresponding heat dissipation fan has a corresponding electric parameter fault, and then the corresponding heat dissipation fan is determined as the fault fan, and it is determined that the fault type of the heat dissipation fan is the corresponding electric parameter fault; when the vibration parameter curve of any one heat dissipation fan meets the preset abnormal curve condition, it is determined that the corresponding heat dissipation fan has a corresponding vibration parameter fault, and then the corresponding heat dissipation fan is determined as the fault fan, and it is determined that the fault type of the heat dissipation fan is the corresponding vibration parameter fault (such as fan blade damage fault), the working state of the heat dissipation fan is automatically judged, and the specific fault content of the fault fan is analyzed to determine the corresponding fault type. In addition, the fault data of the corresponding heat dissipation fan can be used as a reference for fan upgrade design to optimize the design of the heat dissipation fan.

[0037] In one embodiment, the air-cooled energy storage system includes a plurality of battery clusters, each battery cluster including a plurality of battery packs, each battery pack including at least one heat dissipation fan; each battery cluster is provided with battery cluster number information, each battery pack is provided with battery pack number information, and each heat dissipation fan is provided with fan number information.

[0038] In one embodiment, the air-cooled energy storage system includes a plurality of battery clusters, each battery cluster including a plurality of battery packs, each battery pack including at least one heat dissipation fan; each battery cluster is provided with battery cluster number information, each battery pack is provided with battery pack number information, and each heat dissipation fan is provided with fan number information.

[0039] The battery cluster is provided with battery cluster number information (for example, the battery cluster number information of the first cluster is 001), the battery pack is provided with battery pack number information (for example, the battery pack number information of the first pack is 001), and the heat dissipation fan is provided with fan number information (for example, the fan number information of the first fan is 001). It should be noted that the air-cooled energy storage system is provided with system number, for example, the system number is XXXX. For example, the number combination of the first fan of the first cluster and the first pack is; that is, XXXX-001-001-001.

[0040] As shown in Figure 3 After the step of obtaining the fault type of the corresponding fault fan according to the electrical parameter curve and the vibration parameter curve of the corresponding fault fan, the step includes: Step S310, obtaining the fan number information, the battery pack number information and the battery cluster number information of the corresponding fault fan.

[0041] For example, the battery pack is provided with a BMU (Battery Management Unit, battery management unit) control board, the BMU control board includes a first processing module (such as a first MCU (Microcontroller Unit, microcontroller unit)) and at least one second processing module (such as a second MCU), the first processing module is connected to each second processing module of the corresponding battery pack, and the second processing module is connected to the corresponding heat dissipation fan; the first processing module is provided with battery pack number information, and the second processing module is provided with fan number information. The battery cluster is provided with a high-voltage box, the high-voltage box is connected to each first processing module of the corresponding battery cluster, and the high-voltage box is also used to connect a BMS (Battery Management System, battery management system) master control, and the high-voltage box is provided with battery cluster number information. When the fault fan is detected, the second processing module transmits the corresponding fan number information and fault information to the first processing module, the first processing module transmits the corresponding battery pack number information, the fan number information and the fault information to the corresponding high-voltage box, the high-voltage box transmits the corresponding battery cluster number information, the battery pack number information, the fan number information and the fault information to the BMS master control, and then the BMS master control obtains the fan number information, the battery pack number information and the battery cluster number information of the corresponding fault fan.

[0042] Step S320, obtaining the positioning information of the fault fan according to the corresponding fan number information, the battery pack number information and the battery cluster number information.

[0043] For example, the fan number information, the battery pack number information and the battery cluster number information of the corresponding fault fan can be combined to obtain the positioning information of the fault fan.

[0044] Step S330, according to the positioning information, the fan icon corresponding to the fault fan is marked, and the marked fan icon is displayed.

[0045] For example, the BMS master control is connected with the BMS display screen, the BMS can send the positioning information to the BMS display screen, and then the BMS display screen marks the fan icon corresponding to the fault fan according to the positioning information, and displays the marked fan icon on the BMS display screen. For another example, the BMS master control is connected with the EMS (Energy Management System), and the EMS is provided with a display screen. The EMS marks the fan icon corresponding to the fault fan according to the positioning information, and displays the marked fan icon on the display screen of the EMS, so as to realize remote judgment and identification of the running state of the cooling fan in the field in combination with the BMS display screen and the EMS, to find the abnormality of the cooling fan in advance, prevent the charging and discharging of the equipment in the field from being affected due to the abnormality of the fan, and improve the user experience.

[0046] For example, the BMS master control attaches the data packet uploaded by each high-voltage box to the system number of the current BMS, and then packs it into a data packet and sends it to the BMS display screen and the EMS. The BMS display screen and the EMS analyze and process the data packet based on the preset communication protocol of the BMS master control, so as to locate the fault fan, mark the fan icon corresponding to the fault fan, and display the marked fan icon through the corresponding display screen. Since each data packet has a number on the source path, the working state of the cooling fan of each battery pack in the air-cooled energy storage system can be displayed through the analyzed data, so as to realize rapid positioning and display of the fault fan.

[0047] In one embodiment, the step of marking the fan icon corresponding to the fault fan according to the positioning information and displaying the marked fan icon comprises: According to the positioning information, the fan icon corresponding to the fault fan is color marked, and the color marked fan icon and the fault type are displayed.

[0048] For example, the fault type can be represented by a fault number, such as: 0X01 for normal fan operation, 0X02 for fan stall, 0X03 for fan power without voltage, 0X04 for fan blade damage, etc.

[0049] For example, green represents that the cooling fan is in a normal working state, and red represents that the cooling fan is in an abnormal state (i.e. red represents that the corresponding cooling fan is a fault fan). The position of the fault fan can be located by color judgment, and the current abnormal state of the fan can be judged according to the display of the fault type of the fan on the display screen, so as to realize rapid positioning of the fault fan.

[0050] In one embodiment, the electrical parameter data comprises current data, voltage data and power data; the electrical parameter curve comprises a current variation curve corresponding to the current data, a voltage variation curve corresponding to the voltage data and a power variation curve corresponding to the power data.

[0051] The current data can be collected by a current collection module at the power supply end of the corresponding cooling fan; the voltage data can be collected by a voltage collection module at the power supply end of the corresponding cooling fan; the power data refers to the power supply power of the corresponding cooling fan, and the power data can be processed according to the current data and the voltage data. For example, the current collection module can be a shunt, and the BMU control board is provided with a fan power supply circuit connected to the corresponding cooling fan. A shunt can be arranged in the fan power supply circuit of the BMU control board, and the shunt can obtain the power supply current of the fan power supply circuit, and thus obtain the current data of the corresponding cooling fan. The fan power supply circuit of the BMU control board is provided with a voltage collection chip, and the voltage collection chip is used to collect the voltage of the fan power supply circuit, and thus obtain the voltage data.

[0052] The current data comprises current collection values at different collection time points, and the current variation curve of the corresponding cooling fan can be obtained by curve fitting processing of the corresponding current data. The voltage data comprises voltage collection values at different collection time points, and the voltage variation curve of the corresponding cooling fan can be obtained by curve fitting processing of the corresponding voltage data. The power data comprises power values at different collection time points, and the power variation curve of the corresponding cooling fan can be obtained by curve fitting processing of the corresponding power data. It should be noted that the current variation curve can be obtained by establishing a two-dimensional coordinate system, with time as the horizontal coordinate and current amplitude as the vertical coordinate; the voltage variation curve can be obtained by establishing a two-dimensional coordinate system, with time as the horizontal coordinate and voltage amplitude as the vertical coordinate; and the power variation curve can be obtained by establishing a two-dimensional coordinate system, with time as the horizontal coordinate and power amplitude as the vertical coordinate.

[0053] As shown in FIG. 4, the step of obtaining the fault type of the fault fan according to the electrical parameter curve and the vibration parameter curve of the corresponding fault fan comprises: Figure 4 Step S410, extracting feature values from the current variation curve, the voltage variation curve and the power variation curve to obtain current feature values, voltage feature values and power feature values.

[0054] The current feature values of the corresponding cooling fan can be obtained by extracting feature values from the current variation curve; the voltage feature values of the corresponding cooling fan can be obtained by extracting feature values from the voltage variation curve; and the power feature values of the corresponding cooling fan can be obtained by extracting feature values from the power variation curve. It should be noted that the feature values can be curve slope change values.

[0055] ​Step S420, when the current characteristic value falls within the first current threshold range, the voltage characteristic value falls within the first voltage threshold range, and the power characteristic value falls within the first power threshold range, it is determined that the fault type of the fault fan is fan stall.

[0056] For example, when the heat dissipation fan stalls, since the heat dissipation fan converts the original electric energy into mechanical energy and heat energy, the energy type changes will cause the corresponding current change curve, voltage change curve and power change curve to change. By comparing the current characteristic value with the first current threshold range, comparing the voltage characteristic value with the first voltage threshold range, comparing the power characteristic value with the first power threshold range, and according to the comparison result, when the current characteristic value falls within the first current threshold range, the voltage characteristic value falls within the first voltage threshold range, and the power characteristic value falls within the first power threshold range, it is determined that the fault type of the fault fan is fan stall, which realizes accurate and rapid determination of the stall fault of the fan.

[0057] Step S430, when the current characteristic value falls within the second current threshold range, the voltage characteristic value falls within the second voltage threshold range, and the power characteristic value falls within the second power threshold range, it is determined that the fault type of the fault fan is fan open circuit.

[0058] When the heat dissipation fan has an open circuit, the heat dissipation fan changes from the original state of having voltage and having current to the state of having voltage and having no current, so it can be judged that the internal coil of the heat dissipation fan has an open circuit. For example, by comparing the current characteristic value with the second current threshold range, comparing the voltage characteristic value with the second voltage threshold range, comparing the power characteristic value with the second power threshold range, when the current characteristic value falls within the second current threshold range, the voltage characteristic value falls within the second voltage threshold range, and the power characteristic value falls within the second power threshold range, it is determined that the corresponding heat dissipation fan changes from the state of having voltage and having current to the state of having voltage and having no current, and then it is determined that the corresponding heat dissipation fan is a fault fan, and the fault type of the fault fan is fan open circuit, which realizes accurate and rapid determination of the open circuit fault of the fan.

[0059] In one example, if the current characteristic value falls within the third current threshold range, the voltage characteristic value falls within the third voltage threshold range, and the power characteristic value falls within the third power threshold range, it is determined that the obtained current change curve, voltage change curve and power change curve are in a relatively smooth curve, and then it is determined that the corresponding heat dissipation fan is in normal operation.

[0060] In one embodiment, the vibration parameter data includes vibration data of a first direction axis, vibration data of a second direction axis, and vibration data of a third direction axis; the vibration parameter curves include a first direction axis vibration curve corresponding to the first direction axis vibration data, a second direction axis vibration curve corresponding to the second direction axis vibration data, and a third direction axis vibration curve corresponding to the third direction axis vibration data.

[0061] The vibration parameter data can be acquired through an acceleration sensing module, which can be a three-axis acceleration sensing module. The three-axis acceleration sensing module is used to measure the motion acceleration of the corresponding cooling fan in the X-axis, Y-axis, and Z-axis directions, thereby obtaining vibration data of the first direction axis (X-axis), the second direction axis (Y-axis), and the third direction axis (Z-axis).

[0062] The first directional axis vibration data includes acceleration values ​​collected at different acquisition time points along the X-axis. Curve fitting is performed on the corresponding first directional axis vibration data to obtain the first directional axis vibration curve of the corresponding cooling fan. The second directional axis vibration data includes acceleration values ​​collected at different acquisition time points along the X-axis. Curve fitting is performed on the corresponding second directional axis vibration data to obtain the second directional axis vibration curve of the corresponding cooling fan. The third directional axis vibration data includes acceleration values ​​collected at different acquisition time points along the X-axis. Curve fitting is performed on the corresponding third directional axis vibration data to obtain the third directional axis vibration curve of the corresponding cooling fan. It should be noted that the first directional axis vibration curve can be obtained using a two-dimensional coordinate system, with the horizontal axis representing time and the vertical axis representing the acceleration amplitude of the first directional axis; the second directional axis vibration curve can be obtained using a two-dimensional coordinate system, with the horizontal axis representing time and the vertical axis representing the acceleration amplitude of the second directional axis; the third directional axis vibration curve can be obtained using a two-dimensional coordinate system, with the horizontal axis representing time and the vertical axis representing the acceleration amplitude of the third directional axis.

[0063] like Figure 5 As shown, the step of determining the fault type of a faulty fan based on its electrical parameter curve and vibration parameter curve also includes: Step S510: Extract feature values ​​from the vibration curves of the first, second, and third directional axes to obtain the first, second, and third vibration feature values.

[0064] The first vibration characteristic value of the corresponding heat dissipation fan is obtained by extracting the characteristic value of the first direction axis vibration curve. The second vibration characteristic value of the corresponding heat dissipation fan is obtained by extracting the characteristic value of the second direction axis vibration curve. The third vibration characteristic value of the corresponding heat dissipation fan is obtained by extracting the characteristic value of the third direction axis vibration curve. It should be noted that the characteristic value can be the slope change value of the corresponding curve.

[0065] When the first vibration characteristic value falls within the first vibration threshold range, the second vibration characteristic value falls within the second vibration threshold range, and the third vibration characteristic value falls within the third vibration threshold range, it is determined that the fault type of the fault fan is fan damage.

[0066] For example, a three-axis acceleration sensor is arranged on the top surface of the heat dissipation fan. Based on the vibration of the heat dissipation fan being relatively stable during normal operation and the dynamic balance state being normal, when the fan blade is damaged and the fan is operated with missing corners, the original dynamic balance state of the heat dissipation fan is broken, and the frequency and acceleration of the vibration are also different. The first direction axis vibration curve, the second direction axis vibration curve, and the third direction axis vibration curve corresponding to the acceleration and time are obtained by detecting the acceleration data of each axis of the heat dissipation fan through the three-axis acceleration sensor. By comparing and analyzing the characteristic values of the curves, the first vibration characteristic value is compared with the first vibration threshold range, the second vibration characteristic value is compared with the second vibration threshold range, and the third vibration characteristic value is compared with the third vibration threshold range. When the first vibration characteristic value falls within the first vibration threshold range, the second vibration characteristic value falls within the second vibration threshold range, and the third vibration characteristic value falls within the third vibration threshold range, it is determined that the corresponding heat dissipation fan is a fault fan, and the fan type of the fault fan is fan damage, thereby accurately and quickly determining the fan blade damage fault of the fan.

[0067] In one embodiment, after the step of identifying and processing each characteristic parameter curve based on the preset curve model, the method further comprises: According to each characteristic parameter curve, the machine learning processing is performed on the preset curve model to update the preset curve model.

[0068] The machine learning can be implemented by using existing machine learning algorithms, for example, the machine learning can use a regression algorithm.

[0069] For example, each characteristic parameter curve is stored in a database, and each characteristic parameter curve is extracted from the database for machine learning processing on the preset curve model to optimize the preset curve model, so that the speed and accuracy of the fault detection and judgment of the heat dissipation fan are further improved, and the accuracy and efficiency of the heat dissipation fan fault detection are improved.

[0070] It should be understood that, although Figures 2 to 5The steps in the flowchart are shown in sequence according to the arrows, but the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, Figures 2 to 5 At least part of the steps in the flowchart can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the sub-steps or stages is not necessarily sequential, but can be alternately executed with at least part of other steps or sub-steps or stages of other steps.

[0071] In one embodiment, a battery pack fan fault detection device is provided, comprising: A data acquisition unit is configured to acquire characteristic parameter data of each cooling fan in the air-cooled energy storage system.

[0072] A curve processing unit is configured to perform curve fitting processing on each characteristic parameter data to obtain a characteristic parameter curve corresponding to each cooling fan.

[0073] A fault detection unit is configured to identify each characteristic parameter curve based on a preset curve model, and determine that a corresponding cooling fan is a fault fan when the characteristic parameter curve of any cooling fan meets a preset abnormal curve condition.

[0074] For specific limitations of the battery pack fan fault detection device, refer to the limitations of the battery pack fan fault detection method described above, which will not be repeated here. Each module in the above battery pack fan fault detection device can be realized by software, hardware, and combinations thereof, in whole or in part. The above modules can be embedded in or independent of the processing device in the battery pack fan fault detection system in hardware form, or can be stored in the memory in the battery pack fan fault detection system in software form, so as to be called and executed by the processor to perform the operations corresponding to each of the above modules.

[0075] In one embodiment, as Figure 1 shown, a battery pack fan fault detection system is also provided, applied to an air-cooled energy storage system, the air-cooled energy storage system comprising a plurality of cooling fans 300; the battery pack fan fault detection system comprising a processing device 100 and a plurality of acquisition modules 200, the processing device 100 being connected to each acquisition module 200; each acquisition module 200 being arranged in one-to-one correspondence with each cooling fan 300; the acquisition module 200 being configured to detect characteristic parameter data of the corresponding cooling fan 300; the processing device 100 being configured to execute the steps of the battery pack fan fault detection method of any one of the above.

[0076] The specific description of the processing device 100 and the acquisition module 200 can refer to the description of the above embodiment, and will not be repeated here.

[0077] By setting the acquisition module 200 on the corresponding heat dissipation fan 300, the acquisition module 200 is connected to the processing device 100, the acquisition module 200 collects the characteristic parameter data of the corresponding heat dissipation fan 300 in the air-cooled energy storage system in real time, and transmits the collected characteristic parameter data to the processing device 100. The processing device 100 performs curve fitting processing on each characteristic parameter data obtained, and obtains a characteristic parameter curve corresponding to each heat dissipation fan 300. The processing device 100 identifies each characteristic parameter curve based on a preset curve model, and determines that the corresponding heat dissipation fan 300 is a fault fan when the characteristic parameter curve of any heat dissipation fan 300 meets the preset abnormal curve condition, thereby realizing timely and accurate identification of the fault fan. The application realizes automatic determination of the working condition of the fan by performing curve processing on the characteristic parameter data of the heat dissipation fan 300 and identifying the corresponding characteristic parameter curve, timely feedbacks the working state of the fan, simplifies the fan troubleshooting process, can timely find the fault fan, and has high efficiency and high accuracy in fan fault identification, thereby avoiding the influence of fan damage on the normal operation of the air-cooled energy storage system.

[0078] In one embodiment, as shown in Figure 6 and Figure 7 The acquisition module includes a current acquisition module 202, a voltage acquisition module 204, and an acceleration sensing module 206. The current acquisition module 202 and the voltage acquisition module 204 are connected to the power supply end of the corresponding heat dissipation fan, and the acceleration sensing module 206 is arranged on one side of the corresponding heat dissipation fan. The current acquisition module 202, the voltage acquisition module 204, and the acceleration sensing module 206 are connected to the processing device.

[0079] For example, the current acquisition module 202 can be a shunt, the voltage acquisition module 204 can be a voltage acquisition chip, the power supply end of the heat dissipation fan is connected to a power supply, and the shunt is connected in series between the power supply end of the corresponding heat dissipation fan and the power supply. The voltage acquisition chip is connected between the power supply end of the corresponding heat dissipation fan and the power supply. It should be noted that the power supply can be a 24V power supply. The acceleration sensing module 206 can be a three-axis acceleration sensing module 206, and the acceleration sensing module 206 is arranged on the top surface of the corresponding heat dissipation fan.

[0080] The current collection module 202, the voltage collection module 204 and the acceleration sensing module 206 are connected to the processing device. The current collection module 202 collects current data of the corresponding heat dissipation module and transmits the current data to the processing device. The voltage collection module 204 collects voltage data of the corresponding heat dissipation module and transmits the voltage data to the processing device. The acceleration sensing module 206 collects vibration parameter data of the corresponding heat dissipation module and transmits the vibration parameter data to the processing device. The processing device obtains power data according to the current data and the voltage data. The processing device performs curve fitting processing on the current data, the voltage data, the power data and the vibration parameter data to obtain a current change curve, a voltage change curve, a power change curve and a vibration parameter curve. Then, according to the current change curve, the voltage change curve, the power change curve and the vibration parameter curve, it is determined whether the corresponding fan is a faulty fan and the fault type of the faulty fan is determined, so that the faulty fan can be found in time and the efficiency and accuracy of fan fault identification are improved.

[0081] In one example, the processing device can include a BMU control board connected to the corresponding battery pack, and further include a high-voltage box connected to the corresponding battery cluster and a BMS master control connected to the EMS. The BMU control board includes a first processing module and at least one second processing module. The first processing module is connected to each second processing module of the corresponding battery pack. The second processing module is connected to the corresponding heat dissipation fan. The first processing module is connected to the corresponding high-voltage box.

[0082] For example, the second processing module performs curve drawing on the data transmitted by the corresponding current collection module 202, voltage collection module 204 and acceleration sensing module 206, and determines whether the heat dissipation fan is faulty through the fault detection method in the above embodiment. The specific abnormal fault type (such as 0X01 indicating that the fan is working normally, 0X02 indicating that the fan is blocked, 0X03 indicating that the fan has no power supply voltage, and 0X04 indicating that the fan blade is damaged) is data calibrated and uploaded to the first processing module. For example, the second processing module packs and arranges the fault type and fan number data into a first data packet and sends it to the first processing module. The first processing module packs the corresponding battery pack number data with the first data packet to obtain a second data packet, and transmits the second data packet to the corresponding cluster high-voltage box. The high-voltage box attaches the battery cluster number data to the second data packet, packs it into a third data packet, and tests the third data packet to the BMS master control. The BMS master control attaches the system number data of the current BMS to the data packet uploaded by each high-voltage box, packs it into a fourth data packet, and sends it to the BMS display screen and the EMS. The BMS display screen and the EMS analyze the fourth data packet according to the communication protocol of the BMS master control and display it on the display screen, so as to realize rapid positioning and display of the faulty fan.

[0083] In one embodiment, a computer storage medium is also provided, having stored thereon a computer program, which, when executed by a processor, implements the steps of the battery pack fan fault detection method of any one of the above.

[0084] For example, the computer program, when executed by a processor, implements the following steps: Obtaining characteristic parameter data of each heat dissipation fan in the air-cooled energy storage system; performing curve fitting processing on each characteristic parameter data to obtain a characteristic parameter curve corresponding to each heat dissipation fan; performing identification processing on each characteristic parameter curve based on a preset curve model, and determining a corresponding heat dissipation fan as a fault fan when the characteristic parameter curve of any heat dissipation fan meets a preset abnormal curve condition, thereby realizing timely and accurate identification of the fault fan.

[0085] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments of each division operation method. Any reference to a memory, storage, database, or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0086] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present disclosure.

[0087] The above embodiments only express several implementation ways of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation to the patent scope of the application. It should be pointed out that for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, which all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A battery pack fan fault detection method, characterized by, The method comprises the following steps: Obtaining characteristic parameter data of each cooling fan in the air-cooled energy storage system; Performing curve fitting processing on each characteristic parameter data to obtain a characteristic parameter curve corresponding to each cooling fan; Based on a preset curve model, identifying each characteristic parameter curve, and determining that the corresponding cooling fan is a fault fan when the characteristic parameter curve of any cooling fan meets a preset abnormal curve condition.

2. The battery pack fan fault detection method of claim 1, wherein, The characteristic parameter data includes electrical parameter data and vibration parameter data; The step of performing curve fitting processing on each characteristic parameter data to obtain a characteristic parameter curve corresponding to each cooling fan comprises: Performing curve fitting processing on each electrical parameter data and each vibration parameter data to obtain an electrical parameter curve corresponding to each cooling fan and a vibration parameter curve corresponding to each cooling fan; The step of determining that the corresponding cooling fan is a fault fan when the characteristic parameter curve of any cooling fan meets a preset abnormal curve condition comprises: Obtaining a fault type of the fault fan according to the electrical parameter curve and the vibration parameter curve corresponding to the fault fan.

3. The battery pack fan fault detection method of claim 2, wherein, The air-cooled energy storage system comprises a plurality of battery clusters, each battery cluster comprises a plurality of battery packs, each battery pack comprises at least one cooling fan, each battery cluster is provided with battery cluster number information, each battery pack is provided with battery pack number information, and each cooling fan is provided with fan number information; The step of obtaining a fault type of the fault fan according to the electrical parameter curve and the vibration parameter curve corresponding to the fault fan comprises: Obtaining fan number information, battery pack number information, and battery cluster number information corresponding to the fault fan; Obtaining positioning information of the fault fan according to the corresponding fan number information, battery pack number information, and battery cluster number information; According to the positioning information, marking a fan icon corresponding to the fault fan, and displaying the marked fan icon.

4. The battery pack fan fault detection method of claim 3, wherein, The step of marking a fan icon corresponding to the fault fan according to the positioning information, and displaying the marked fan icon comprises: According to the positioning information, color marking a fan icon corresponding to the fault fan, and displaying the color marked fan icon and the fault type.

5. The battery pack fan fault detection method of claim 2, wherein, The electrical parameter data includes current data, voltage data, and power data; and the electrical parameter curve includes a current change curve corresponding to the current data, a voltage change curve corresponding to the voltage data, and a power change curve corresponding to the power data; The step of obtaining a fault type of the fault fan according to the electrical parameter curve and the vibration parameter curve corresponding to the fault fan comprises: Extracting feature values from the current change curve, the voltage change curve, and the power change curve to obtain current feature values, voltage feature values, and power feature values; and determining that the fault type of the faulted fan is fan stall when the current characteristic value falls within a first current threshold range, the voltage characteristic value falls within a first voltage threshold range, and the power characteristic value falls within a first power threshold range; determining that the fault type of the faulted fan is fan stall when the current characteristic value falls within a first current threshold range, the voltage characteristic value falls within a first voltage threshold range, and the power characteristic value falls within a first power threshold range; 6. The battery pack fan fault detection method of claim 2, wherein, the vibration parameter data comprises first direction axis vibration data, second direction axis vibration data, and third direction axis vibration data; the vibration parameter curve comprises a first direction axis vibration curve corresponding to the first direction axis vibration data, a second direction axis vibration curve corresponding to the second direction axis vibration data, and a third direction axis vibration curve corresponding to the third direction axis vibration data; the step of obtaining the fault type of the faulted fan according to the electrical parameter curve and the vibration parameter curve corresponding to the faulted fan further comprises: extracting characteristic values from the first direction axis vibration curve, the second direction axis vibration curve, and the third direction axis vibration curve to obtain first vibration characteristic values, second vibration characteristic values, and third vibration characteristic values; determining that the fault type of the faulted fan is fan stall when the first vibration characteristic value falls within a first vibration threshold range, the second vibration characteristic value falls within a second vibration threshold range, and the third vibration characteristic value falls within a third vibration threshold range.

7. The battery pack fan fault detection method of any one of claims 1-6, wherein, the step of identifying each characteristic parameter curve based on the preset curve model further comprises: performing machine learning processing on the preset curve model according to each characteristic parameter curve to update the preset curve model.

8. A battery pack fan fault detection system, comprising: The air-cooled energy storage system comprises a plurality of cooling fans; the battery pack fan fault detection system comprises a processing device and a plurality of acquisition modules, the processing device is connected to each acquisition module; each acquisition module is arranged one-to-one corresponding to each cooling fan; the acquisition module is used to detect the characteristic parameter data of the corresponding cooling fan; the processing device is used to execute the steps of the battery pack fan fault detection method according to any one of claims 1 to 7.

9. The battery pack fan fault detection system of claim 8, wherein, The acquisition module comprises a current acquisition module, a voltage acquisition module, and an acceleration sensing module; the current acquisition module and the voltage acquisition module are connected to the power supply end of the corresponding cooling fan, and the acceleration sensing module is arranged on one side of the corresponding cooling fan; the current acquisition module, the voltage acquisition module, and the acceleration sensing module are connected to the processing device.

10. A computer storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to realize the steps of the battery pack fan fault detection method according to any one of claims 1 to 7.

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