Battery replacement cabinet intelligent fault monitoring method and device and storage medium

Abstract

The present application relates to the technical field of failure prediction, and particularly relates to a battery swap cabinet intelligent failure monitoring method and device and a storage medium, the method comprising: collecting battery swap cabinet port data and shell data; extracting waveform features of voltage waveforms at the moment of insertion, and constructing arc intensity features according to the waveform features; constructing a shell abnormality index based on shell contact pressure, and judging a shell fatigue state; constructing a shell wear index according to vibration signals at the moment of insertion, and constructing a thermal shock index according to a rate of change of shell temperature at the beginning of a charging session, and updating the shell fatigue state based on the shell wear index and the thermal shock index; performing failure early warning according to the arc intensity features and the shell fatigue state within a management period, and adjusting target shell contact pressure in the next management period according to a failure early warning result and shell deformation data. The present application improves the operation efficiency of the battery swap cabinet.

Description

Technical Field

[0001] This invention relates to the field of fault prediction technology, and in particular to an intelligent fault monitoring method, device and storage medium for battery swapping cabinets. Background Technology

[0002] Currently, battery swapping cabinet ports, especially the spring contact systems for battery pack insertion and removal interfaces, generally lack effective online monitoring and early warning mechanisms. Traditional maintenance methods mainly rely on periodic inspections and post-incident repairs, which are insufficient to address progressive failures caused by the combined effects of multiple factors such as mechanical wear, contact pressure attenuation, arc erosion, and thermal shock resulting from frequent insertions and removals.

[0003] Existing solutions are mostly limited to monitoring a single parameter (such as on / off state), which cannot fully capture the potential risks under the combined electrical-mechanical-thermal working state of the connector. This results in insufficient accuracy in fault prediction, high maintenance costs, and safety concerns caused by connection deterioration. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent fault monitoring method, device and storage medium for battery swapping cabinets, so as to solve at least one of the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for intelligent fault monitoring of battery swapping cabinets includes:

[0007] Collect data from the battery swapping cabinet ports and contact springs;

[0008] Extract waveform features of the voltage waveform at the moment of insertion, and construct arc intensity features based on the waveform features;

[0009] Based on the contact pressure of the spring, an abnormal index of the spring is constructed, and the fatigue state of the spring is determined.

[0010] The wear index of the spring is constructed based on the vibration signal at the moment of insertion, and the thermal shock index is constructed based on the temperature change rate of the spring at the beginning of the charging session. The fatigue state of the spring is updated based on the wear index and the thermal shock index.

[0011] Fault warnings are generated based on the arc intensity characteristics and spring fatigue status within the management cycle, and the target spring contact pressure for the next management cycle is adjusted based on the fault warning results and spring deformation data.

[0012] Optionally, the minimum voltage value Vmi of the voltage waveform at the moment of the i-th insertion within the management cycle is extracted, and the peak voltage drop ΔVi is calculated, where ΔVi = Vs - Vmi, and Vs is the rated voltage.

[0013] Starting from the trigger point, search backwards to find the moment when the voltage first drops below Vs×95%, denoted as Tsi. Starting from Tsi, search backwards to find the moment when the voltage first enters and remains within 98%×Vs-102%×Vs for more than 10 milliseconds, denoted as Tei. The time difference between Tei and Tsi is taken as the voltage drop duration, denoted as Tdi.

[0014] The arc intensity index ASi is constructed based on the peak voltage drop ΔVi and the voltage drop duration Tdi. ASi is set as x1×ΔVi / Vs+x2×tanh(Tdi / Ty). The arc intensity indices within the management period are sorted, and the maximum value is taken as the arc intensity characteristic of the current management period, denoted as AS.

[0015] Where Ty is the preset duration, x1 is the fall weight, x2 is the duration weight, and x1+x2=1.

[0016] Optionally, the average contact pressure of the spring sheet during the management cycle is calculated and denoted as Pa, and the spring sheet abnormality index SF is constructed, with SF=max(0,1-Pa / Pe);

[0017] When SF is less than or equal to the preset fatigue index s0, the fatigue state of the spring in the current management cycle is determined to be normal; otherwise, the fatigue state of the spring in the current management cycle is determined to be abnormal.

[0018] Where Pe is the rated contact pressure of the spring.

[0019] Optionally, the spring wear index is constructed based on the vibration signal at the moment of insertion during the management cycle:

[0020] The root mean square value (RMSa) of the vibration signal acceleration at each insertion instant within the management cycle is calculated, and the spring wear index is constructed based on the reference value RMSb of the root mean square value of the vibration signal acceleration at the spring insertion instant.

[0021] When RMSa / RMSb is less than or equal to 1, the spring wear index is set to 0; otherwise, the spring wear index is set to [1-exp(1-RMSa / RMSb)].

[0022] Optionally, a thermal shock index can be constructed based on the rate of change of contact temperature at the beginning of each charging session within the management cycle:

[0023] The maximum value of the change rate of the contact spring temperature at the beginning of the j-th charging session within the management cycle is Tmj. This value is then compared with the preset temperature change rate t. If Tmj is less than or equal to t, the change rate of the contact spring temperature at the beginning of the charging session is considered normal. Otherwise, the change rate of the contact spring temperature at the beginning of the charging session is considered abnormal. The number of charging sessions with abnormal contact spring temperature change rates within the management cycle is counted as m1, and the number of charging sessions within the management cycle is counted as m2. The ratio of m1 to m2 is used as the thermal shock index.

[0024] Optionally, anomaly factors are determined based on the wear index and thermal shock index of the spring sheet, and the fatigue state of the spring sheet is updated based on the anomaly factors:

[0025] The expression for the abnormal factor is: u = w1 × spring wear index + w2 × thermal shock index, where w1 is the wear weight, w2 is the thermal shock weight, and w1 + w2 = 1.

[0026] The preset fatigue index is updated based on the abnormal factors, and the updated preset fatigue index is set as s1.

[0027] Optionally, a risk coefficient is constructed based on the arc intensity characteristics and the fatigue state of the spring sheet during the management cycle, and a fault warning is given based on the risk coefficient:

[0028] When the fatigue state of the spring is normal, the risk factor is set to F1, and F1 = u1 × min(1, AS / a0).

[0029] When the shrapnel is in an abnormal state, the risk factor is set to F2, and F2 is set to u1×min(1,AS / a0)+u2×lg[3×(shrapnel abnormality index-preset fatigue index)+1] / lg4;

[0030] Where u1 is the arc weight, u2 is the spring fatigue weight, u1+u2=1, and a0 is the preset arc intensity;

[0031] When the risk coefficient is greater than or equal to the preset risk coefficient, a fault risk warning will be issued to the user; otherwise, no fault risk warning will be issued to the user.

[0032] Optionally, the average value με of the spring deformation data of all successfully inserted events within the management period is calculated, the standard deviation σε of the spring deformation data of all successfully inserted events within the management period is calculated, and the structural health index SH is constructed.

[0033] When issuing a fault risk warning to the user during the current management cycle, if the structural health index SH is less than or equal to the preset structural health index, the target spring contact pressure for the next management cycle will be adjusted to P1, where P1 = p0 × (1 + β), p0 is the target spring contact pressure for the current management cycle, and β is the preset correction coefficient; if the structural health index SH is greater than the preset structural health index, the target spring contact pressure for the next management cycle will not be adjusted.

[0034] If no fault risk warning is given to the user during the current management cycle, the target spring contact pressure will not be adjusted for the next management cycle.

[0035] According to another aspect of this application, an intelligent fault monitoring device for a battery swapping cabinet is provided, comprising:

[0036] The data acquisition unit is used to collect port data and spring contact data of the battery swapping cabinet;

[0037] An arc feature construction unit is used to extract waveform features of the voltage waveform at the moment of insertion and construct arc intensity features based on the waveform features.

[0038] The spring fatigue analysis unit is used to construct the spring abnormality index based on the spring contact pressure and determine the spring fatigue state.

[0039] The update unit is used to construct the spring wear index based on the vibration signal at the moment of insertion, and to construct the thermal shock index based on the spring temperature change rate at the beginning of the charging session, and to update the spring fatigue state based on the spring wear index and the thermal shock index.

[0040] The fault monitoring unit is used to provide fault warnings based on the arc intensity characteristics and spring fatigue status within the management cycle, and to adjust the target spring contact pressure for the next management cycle based on the fault warning results and spring deformation data.

[0041] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein the computer program is used to control the electronic device where the computer-readable storage medium is located to execute the intelligent fault monitoring method for the battery swapping cabinet during runtime.

[0042] The beneficial effects of this invention are as follows: By constructing an intelligent monitoring system that integrates multi-source information perception, dynamic feature extraction, and adaptive control, accurate assessment and proactive maintenance of the health status of the battery swapping cabinet ports are achieved. Its core value lies in the deep fusion analysis of isolated electrical parameters and mechanical conditions, dynamically correcting the spring fatigue model, and proactively adjusting operating parameters based on comprehensive risk assessment results. This significantly improves the accuracy and timeliness of fault early warning, effectively extends the service life of key components, transforms the operation and maintenance mode from passive response to proactive intervention, and ultimately ensures the high reliability and safe, economical operation of charging facilities. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating the intelligent fault monitoring method for the battery swapping cabinet in this embodiment.

[0045] Figure 2 This is a flowchart illustrating the method for constructing the updated coefficients in this embodiment.

[0046] Figure 3 This is a flowchart illustrating the fault monitoring method in this embodiment.

[0047] Figure 4 This is a schematic diagram of the intelligent fault monitoring device for the battery swapping cabinet in this embodiment. Detailed Implementation

[0048] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments and accompanying drawings, further clarifies the invention. Similar components in the drawings are indicated by the same reference numerals. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.

[0049] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0050] Specifically, this embodiment is applied to battery swapping cabinet systems, particularly for charging and swapping scenarios of electric bicycle charging packs. As a frequently used public infrastructure, the reliability of the battery swapping cabinet's ports and contacts directly affects charging safety and service life. Through intelligent fault monitoring, problems such as abnormal electrical connections, mechanical wear, and thermal shock can be detected in real time, making it suitable for the management of charging facilities that require high reliability and low maintenance costs.

[0051] Please see Figure 1 As shown, it is a flowchart of the intelligent fault monitoring method for the battery swapping cabinet in this embodiment, including:

[0052] Step S101: Collect battery swapping cabinet port data and spring contact data. The battery swapping cabinet port data is the voltage waveform at the moment of insertion. The voltage waveform at the moment of insertion is the voltage timing data within 500ms of the moment the battery pack is inserted into the contact point, which is used to analyze the arc characteristics. The spring contact data includes spring contact pressure, vibration signal at the moment of insertion, spring contact temperature change rate and spring contact deformation data at the beginning of the charging session. The spring contact pressure is the positive pressure of the spring contact on the battery pack contact, reflecting the tightness of the mechanical connection. The vibration signal at the moment of insertion is the vibration acceleration data within 500ms of the battery pack being inserted and locked, reflecting the impact intensity. The spring contact temperature change rate at the beginning of the charging session is the instantaneous change rate of the spring contact temperature within the first 5 minutes after the start of charging. The spring contact deformation data is the strain at the root of the spring contact.

[0053] Specifically, in this embodiment, the voltage waveform at the moment of insertion can be acquired by high-speed ADC sampling, the contact pressure of the spring can be acquired by MEMS piezoresistive pressure sensor, the vibration signal at the moment of insertion can be acquired by triaxial MEMS accelerometer, the rate of change of spring temperature at the beginning of charging session can be acquired by K-type thermocouple + temperature sampling circuit, and the deformation data of spring can be acquired by foil strain gauge + signal conditioning circuit. In this embodiment, no specific limitation is made on the data acquisition method, and those skilled in the art can set it freely according to their needs.

[0054] Specifically, by comprehensively collecting the voltage waveform and multiple physical parameters of the spring contacts at the moment of insertion, the electrical and mechanical state changes during battery pack insertion can be captured from all angles. This provides a rich data foundation for subsequent analysis, helps to identify potential fault signs early, and improves the comprehensiveness and accuracy of the monitoring system.

[0055] Please continue reading. Figure 1 As shown, the intelligent fault monitoring method for the battery swapping cabinet also includes:

[0056] Step S102: Extract the waveform features of the voltage waveform at the moment of insertion, and construct the arc intensity features based on the waveform features.

[0057] Specifically, extract the minimum voltage value Vmi of the voltage waveform at the moment of the i-th insertion within the management cycle, and calculate the peak voltage drop ΔVi, where ΔVi = Vs - Vmi, and Vs is the rated voltage;

[0058] Starting from the trigger point, search backwards to find the moment when the voltage first drops below Vs×95%, denoted as Tsi. Starting from Tsi, search backwards to find the moment when the voltage first enters and remains within 98%×Vs-102%×Vs for more than 10 milliseconds, denoted as Tei. The time difference between Tei and Tsi is taken as the voltage drop duration, denoted as Tdi.

[0059] The arc intensity index ASi is constructed based on the peak voltage drop ΔVi and the voltage drop duration Tdi. ASi is set as x1×ΔVi / Vs+x2×tanh(Tdi / Ty). The arc intensity indices within the management period are sorted, and the maximum value is taken as the arc intensity characteristic of the current management period, denoted as AS.

[0060] Where Ty is the preset duration, x1 is the fall weight, x2 is the duration weight, and x1+x2=1.

[0061] Specifically, in this embodiment, the preset duration is the voltage drop duration benchmark value, which is set to 20ms, the drop weight is 0.7, and the duration weight is 0.3.

[0062] Specifically, in this embodiment, the management period is 5 days. This embodiment does not impose specific limitations on the setting of the management period, and those skilled in the art can set it freely according to their needs.

[0063] Specifically, by constructing a normalized arc intensity index, the non-stationary transient signal is transformed into a scalar index that can be compared over time. This index directly reflects the degree of air breakdown and energy release at the electrical contact point during a connection event, providing crucial electrical-side quantitative evidence for assessing the cleanliness and oxidation level of the contact surface.

[0064] Please continue reading. Figure 1 As shown, the intelligent fault monitoring method for the battery swapping cabinet also includes:

[0065] Step S103: Construct a spring contact pressure abnormality index and determine the spring fatigue state.

[0066] Specifically, calculate the average contact pressure of the spring sheet during the management cycle, denoted as Pa, and construct the spring sheet anomaly index SF, setting SF=max(0,1-Pa / Pe);

[0067] When SF is less than or equal to the preset fatigue index s0, the fatigue state of the spring in the current management cycle is determined to be normal; otherwise, the fatigue state of the spring in the current management cycle is determined to be abnormal.

[0068] Where Pe is the rated contact pressure of the spring.

[0069] Specifically, in this embodiment, the fatigue index is preset to 0.1.

[0070] Specifically, this step performs a baseline assessment of the static contact pressure of the spring and calculates the spring anomaly index. Essentially, this measures the degree of attenuation of the positive contact force it provides relative to the rated value. This is direct evidence to determine whether the spring has undergone plastic deformation or stress relaxation. This step establishes a static benchmark for the health status of the spring and provides an initial anchor point for subsequent introduction of dynamic wear indicators for condition correction.

[0071] Please continue reading. Figure 1 As shown, the intelligent fault monitoring method for the battery swapping cabinet also includes:

[0072] Step S104: Construct a spring wear index based on the vibration signal at the moment of insertion, and construct a thermal shock index based on the spring temperature change rate at the beginning of the charging session. Update the spring fatigue state based on the spring wear index and the thermal shock index.

[0073] Please see Figure 2 As shown, the method for constructing the update coefficients includes:

[0074] Step S201: Construct the spring wear index based on the vibration signal at the moment of insertion during the management cycle.

[0075] Specifically, the root mean square value (RMSa) of the vibration signal acceleration at each insertion instant within the management cycle is calculated, and the spring wear index is constructed based on the baseline value (RMSb) of the vibration signal acceleration at the spring insertion instant.

[0076] When RMSa / RMSb is less than or equal to 1, the spring wear index is set to 0; otherwise, the spring wear index is set to [1-exp(1-RMSa / RMSb)].

[0077] Specifically, in this embodiment, the root mean square value of the vibration signal acceleration at the moment of insertion of the spring can be set as the root mean square value of the vibration signal acceleration at the moment of insertion of the spring in its brand-new state for the first 100 times. This embodiment does not impose specific limitations on the above setting, and those skilled in the art can set it freely according to their needs.

[0078] Specifically, this step extracts features characterizing mechanical wear from the time-domain vibration signal. The increase in the root mean square value of acceleration relative to the reference value directly reflects the abnormal impact energy and the change in the friction coefficient of the contact surface during insertion and extraction. It is a key prognostic indicator for predicting the mechanical life and connection stability of the spring.

[0079] Please continue reading. Figure 2 As shown, the method for constructing the update coefficients further includes:

[0080] Step S202: Construct a thermal shock index based on the rate of change of spring temperature at the beginning of each charging session within the management cycle.

[0081] Specifically, the maximum value of the change rate of the contact spring temperature at the beginning of the j-th charging session within the management cycle is extracted as Tmj, and it is compared with the preset temperature change rate t. When Tmj is less than or equal to t, the change rate of the contact spring temperature at the beginning of the charging session is determined to be normal; otherwise, the change rate of the contact spring temperature at the beginning of the charging session is determined to be abnormal. The number of charging sessions with abnormal contact spring temperature change rates within the management cycle is counted as m1, and the number of charging sessions within the management cycle is counted as m2. The ratio of m1 to m2 is used as the thermal shock index.

[0082] Specifically, the instantaneous temperature rise rate at the beginning of a charging session is a sensitive function of the contact resistance of the connection point. By statistically analyzing the proportion of abnormal temperature rise events within a cycle, the cumulative damage of thermal stress to the spring material can be effectively quantified, providing a crucial thermal dimension indicator for assessing the long-term conductivity reliability of the connection point.

[0083] Please continue reading. Figure 2 As shown, the method for constructing the update coefficients further includes:

[0084] Step S203: Determine the abnormal factors based on the wear index and thermal shock index of the spring sheet, and update the fatigue state of the spring sheet based on the abnormal factors.

[0085] Specifically, the expression for the abnormal factor is: u = w1 × spring wear index + w2 × thermal shock index, where w1 is the wear weight, w2 is the thermal shock weight, and w1 + w2 = 1.

[0086] The preset fatigue index is updated based on the abnormal factors, and the updated preset fatigue index is set as s1. The value of s1 is set as max[0.1,(s0-η×u)], where η is the preset adjustment coefficient.

[0087] Specifically, in this embodiment, the preset adjustment coefficient is set to 0.3, the wear weight is 0.4, and the thermal shock weight is 0.6.

[0088] Specifically, by pre-setting weights, indicators from two different physical categories, wear and thermal shock, are integrated into a unified anomaly factor. This factor is then used to adaptively correct the static fatigue threshold. Essentially, this enables the system's health assessment model to learn online and adapt to changes in operating conditions, allowing the state determination boundary to dynamically fluctuate with the actual wear situation, thereby enhancing the model's robustness and accuracy.

[0089] Please continue reading. Figure 1 As shown, the intelligent fault monitoring method for the battery swapping cabinet also includes:

[0090] Step S105: Based on the arc intensity characteristics and spring fatigue state within the management cycle, a fault warning is issued, and the target spring contact pressure for the next management cycle is adjusted based on the fault warning result and spring deformation data.

[0091] Please see Figure 3 As shown, the fault monitoring method includes:

[0092] Step S301: Construct a risk coefficient based on the arc intensity characteristics and spring fatigue state during the management cycle, and issue a fault warning based on the risk coefficient.

[0093] Specifically, when the fatigue state of the shrapnel is normal, the risk factor is set to F1, and F1 = u1 × min(1, AS / a0).

[0094] When the shrapnel is in an abnormal state, the risk factor is set to F2, and F2 is set to u1×min(1,AS / a0)+u2×lg[3×(shrapnel abnormality index-preset fatigue index)+1] / lg4;

[0095] Where u1 is the arc weight, u2 is the spring fatigue weight, u1+u2=1, and a0 is the preset arc intensity;

[0096] When the risk coefficient is greater than or equal to the preset risk coefficient, a fault risk warning will be issued to the user; otherwise, no fault risk warning will be issued to the user.

[0097] Specifically, in this embodiment, the arc weight is 0.6, the spring fatigue weight is 0.4, the preset arc intensity is 0.3, and the preset risk coefficient is 0.32.

[0098] Specifically, it uses a weighted algorithm to fuse information from the arc intensity characteristics that characterize instantaneous electrical risks with the fatigue state of the spring fragments that characterizes long-term mechanical risks, generating a comprehensive risk coefficient. This multi-factor decision-making mechanism overcomes the uncertainty of single-parameter early warning, reduces false alarms and missed alarms, and ensures the scientific nature and authority of the early warning signal.

[0099] Please continue reading. Figure 3 As shown, the fault monitoring method further includes:

[0100] Step S302: Construct a structural health index based on the spring deformation data within the management cycle.

[0101] Specifically, the average value με of the spring deformation data of all successfully inserted events within the management period is calculated, the standard deviation σε of the spring deformation data of all successfully inserted events within the management period is calculated, and the structural health index SH is constructed, with the setting: SH=σε / με.

[0102] Specifically, by calculating the coefficient of variation of the strain data at the root of the spring, the structural health index effectively characterizes the consistency of the spring's structural response. The deterioration of this index suggests that the spring may have micro-cracks or fatigue concentration, thus providing a key structural integrity criterion for whether to allow the optimization action of "increasing contact pressure" and preventing the risk of mis-control under structural damage.

[0103] Please continue reading. Figure 3 As shown, the fault monitoring method further includes:

[0104] Step S303: Adjust the target spring contact pressure for the next management cycle based on the fault warning results and structural health index within the management cycle.

[0105] Specifically, when issuing a fault risk warning to the user during the current management cycle, if the structural health index SH is less than or equal to the preset structural health index, the target spring contact pressure for the next management cycle will be adjusted to P1, where P1 = p0 × (1 + β), p0 is the target spring contact pressure for the current management cycle, and β is the preset correction coefficient; if the structural health index SH is greater than the preset structural health index, the target spring contact pressure for the next management cycle will not be adjusted.

[0106] If no fault risk warning is given to the user during the current management cycle, the target spring contact pressure will not be adjusted for the next management cycle.

[0107] Specifically, if the contact pressure of the target shrapnel is greater than the target shrapnel contact pressure threshold, the target shrapnel contact pressure is set to the target shrapnel contact pressure threshold.

[0108] Specifically, at the start of the next management cycle, the adjusted target spring contact pressure is sent to the piezoelectric ceramic actuator of the spring, driving it to adjust its deformation so that the spring contact pressure is stabilized near the adjusted target spring contact pressure.

[0109] Specifically, in this embodiment, the preset structural health index is 0.2, the preset correction coefficient is 0.05, and the target spring contact pressure threshold is 1.2×Pe.

[0110] Specifically, based on the early warning decision results and structural health assessment, the system outputs the optimal target contact pressure setpoint for the next cycle. This mechanism enables the system to proactively maintain connection performance within the optimal range, which is a concrete manifestation of the ultimate value of predictive maintenance.

[0111] Please see Figure 4 As shown, the intelligent fault monitoring device for the battery swapping cabinet includes:

[0112] The data acquisition unit is used to collect port data and spring contact data of the battery swapping cabinet;

[0113] An arc feature construction unit is used to extract waveform features of the voltage waveform at the moment of insertion and construct arc intensity features based on the waveform features.

[0114] The spring fatigue analysis unit is used to construct the spring abnormality index based on the spring contact pressure and determine the spring fatigue state.

[0115] The update unit is used to construct the spring wear index based on the vibration signal at the moment of insertion, and to construct the thermal shock index based on the spring temperature change rate at the beginning of the charging session, and to update the spring fatigue state based on the spring wear index and the thermal shock index.

[0116] The fault monitoring unit is used to provide fault warnings based on the arc intensity characteristics and spring fatigue status within the management cycle, and to adjust the target spring contact pressure for the next management cycle based on the fault warning results and spring deformation data.

[0117] The intelligent fault monitoring device for battery swapping cabinets provided in this application can execute the intelligent fault monitoring method for battery swapping cabinets provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the execution method.

[0118] This application also provides a computer-readable storage medium, which is a tangible physical storage medium that can store the aforementioned computer program and various types of data used in the program; the physical storage medium includes, but is not limited to, existing physical storage media or combinations thereof, such as random access memory, read-only memory, optical disk, and hard disk.

[0119] Those skilled in the art will understand that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technique for storing information (such as computer-readable programs, data structures, program modules, or other data). Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable programs, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and can include any information delivery medium.

[0120] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is impossible to exhaustively list all the implementation methods here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A method for intelligent fault monitoring of a battery swapping cabinet, characterized in that, include: Collect data from the battery swapping cabinet ports and contact springs; Extract waveform features of the voltage waveform at the moment of insertion, and construct arc intensity features based on the waveform features; Based on the contact pressure of the spring, an abnormal index of the spring is constructed, and the fatigue state of the spring is determined. The spring wear index is constructed based on the vibration signal at the moment of insertion during the management cycle: The root mean square value (RMSa) of the vibration signal acceleration at each insertion instant within the management cycle is calculated, and the spring wear index is constructed based on the reference value RMSb of the root mean square value of the vibration signal acceleration at the spring insertion instant. When RMSa / RMSb is less than or equal to 1, the spring wear index is set to 0; otherwise, the spring wear index is set to [1-exp(1-RMSa / RMSb)]. A thermal shock index is constructed based on the rate of change of spring temperature at the beginning of the charging session. The spring fatigue state is updated based on the spring wear index and the thermal shock index. Fault warnings are generated based on the arc intensity characteristics and spring fatigue status within the management cycle, and the target spring contact pressure for the next management cycle is adjusted based on the fault warning results and spring deformation data.

2. The intelligent fault monitoring method for battery swapping cabinets according to claim 1, characterized in that, Extract the minimum voltage Vmi of the voltage waveform at the moment of the i-th insertion within the management cycle, and calculate the peak voltage drop ΔVi, where ΔVi = Vs - Vmi, and Vs is the rated voltage. Starting from the trigger point, search backwards to find the moment when the voltage first drops below Vs×95%, denoted as Tsi. Starting from Tsi, search backwards to find the moment when the voltage first enters and remains within 98%×Vs-102%×Vs for more than 10 milliseconds, denoted as Tei. The time difference between Tei and Tsi is taken as the voltage drop duration, denoted as Tdi. The arc intensity index ASi is constructed based on the peak voltage drop ΔVi and the voltage drop duration Tdi. ASi is set as x1×ΔVi / Vs+x2×tanh(Tdi / Ty). The arc intensity indices within the management period are sorted, and the maximum value is taken as the arc intensity characteristic of the current management period, denoted as AS. Where Ty is the preset duration, x1 is the fall weight, x2 is the duration weight, and x1+x2=1.

3. The intelligent fault monitoring method for battery swapping cabinets according to claim 2, characterized in that, Calculate the average contact pressure of the spring sheet during the management cycle, denoted as Pa, and construct the spring sheet abnormality index SF, setting SF=max(0,1-Pa / Pe); When SF is less than or equal to the preset fatigue index s0, the fatigue state of the spring in the current management cycle is determined to be normal; otherwise, the fatigue state of the spring in the current management cycle is determined to be abnormal. Where Pe is the rated contact pressure of the spring.

4. The intelligent fault monitoring method for battery swapping cabinets according to claim 3, characterized in that, The thermal shock index is constructed based on the rate of change of spring temperature at the beginning of each charging session within the management cycle: The maximum value of the change rate of the contact spring temperature at the beginning of the j-th charging session within the management cycle is Tmj. This value is then compared with the preset temperature change rate t. If Tmj is less than or equal to t, the change rate of the contact spring temperature at the beginning of the charging session is considered normal. Otherwise, the change rate of the contact spring temperature at the beginning of the charging session is considered abnormal. The number of charging sessions with abnormal contact spring temperature change rates within the management cycle is counted as m1, and the number of charging sessions within the management cycle is counted as m2. The ratio of m1 to m2 is used as the thermal shock index.

5. The intelligent fault monitoring method for battery swapping cabinets according to claim 4, characterized in that, Anomalies are determined based on the wear index and thermal shock index of the spring sheet, and the fatigue state of the spring sheet is updated based on these anomalies. The expression for the abnormal factor is: u = w1 × spring wear index + w2 × thermal shock index, where w1 is the wear weight, w2 is the thermal shock weight, and w1 + w2 = 1. The preset fatigue index is updated based on the abnormal factors, and the updated preset fatigue index is set as s1.

6. The intelligent fault monitoring method for battery swapping cabinets according to claim 5, characterized in that, A risk coefficient is constructed based on the arc intensity characteristics and the fatigue state of the spring fragments during the management cycle, and a fault warning is issued based on the risk coefficient: When the fatigue state of the spring is normal, the risk factor is set to F1, and F1 = u1 × min(1, AS / a0). When the fatigue state of the shrapnel is abnormal, the risk coefficient is set to F2, and F2 is set to u1×min(1,AS / a0)+u2×lg[3×(shrapnel abnormality index-preset fatigue index)+1] / lg4; Where u1 is the arc weight, u2 is the spring fatigue weight, u1+u2=1, and a0 is the preset arc intensity; When the risk coefficient is greater than or equal to the preset risk coefficient, a fault risk warning will be issued to the user; otherwise, no fault risk warning will be issued to the user.

7. The intelligent fault monitoring method for battery swapping cabinets according to claim 6, characterized in that, Calculate the average value με of the spring deformation data of all successfully inserted events within the management period, calculate the standard deviation σε of the spring deformation data of all successfully inserted events within the management period, and construct the structural health index SH; When issuing a fault risk warning to the user during the current management cycle, if the structural health index SH is less than or equal to the preset structural health index, the target spring contact pressure for the next management cycle will be adjusted to P1. P1 is set to P0×(1+β), where P0 is the target spring contact pressure for the current management cycle and β is the preset correction coefficient. If the structural health index SH is greater than the preset structural health index, the target spring contact pressure for the next management cycle will not be adjusted. If no fault risk warning is given to the user during the current management cycle, the target spring contact pressure will not be adjusted for the next management cycle.

8. An intelligent fault monitoring device for a battery swapping cabinet, applied to the intelligent fault monitoring method for a battery swapping cabinet as described in any one of claims 1-7, characterized in that, include: The data acquisition unit is used to collect port data and spring contact data of the battery swapping cabinet; An arc feature construction unit is used to extract waveform features of the voltage waveform at the moment of insertion and construct arc intensity features based on the waveform features. The spring fatigue analysis unit is used to construct the spring abnormality index based on the spring contact pressure and determine the spring fatigue state. The update unit is used to construct the spring wear index based on the vibration signal at the moment of insertion, and to construct the thermal shock index based on the spring temperature change rate at the beginning of the charging session, and to update the spring fatigue state based on the spring wear index and the thermal shock index. The fault monitoring unit is used to provide fault warnings based on the arc intensity characteristics and spring fatigue status within the management cycle, and to adjust the target spring contact pressure for the next management cycle based on the fault warning results and spring deformation data.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device on which the computer-readable storage medium is located to execute the intelligent fault monitoring method for the battery swapping cabinet as described in any one of claims 1-7 during runtime.

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