A control system of a battery case cleaning machine and a control method thereof

Abstract

The present application belongs to the technical field of industrial automation control, and particularly relates to a control system and control method of a battery shell cleaning machine, which comprises the following steps: obtaining the sound field signal in the cleaning tank and pre-processing to remove the direct current drift and low-frequency mechanical vibration; calculating the permutation entropy of the sound field signal to evaluate the load complexity, adaptively constructing the penalty factor of the variational mode decomposition according to the load complexity, and then separating the fundamental mode and the high-frequency mode; constructing the risk index reflecting the cavitation erosion degree according to the energy proportion of the high-frequency mode and its time-domain volatility; and adjusting the frequency jitter amplitude and power amplitude of the ultrasonic generator according to the deviation of the risk index and the safety threshold. The present application effectively suppresses the destructive cavitation and resonance damage by adaptively decoupling the damage signal and the double-variable collaborative control strategy, balances the cleaning effect and the workpiece safety under the premise of ensuring high cleaning efficiency.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology. More specifically, this invention relates to a control system and control method for a battery casing cleaning machine. Background Technology

[0002] In the manufacturing process of new energy power batteries, the cleanliness of the battery casing has a significant impact on the subsequent welding quality and the safety of battery use. This requirement is even more stringent for square aluminum casings with a wall thickness of only 0.3mm to 0.5mm. Currently, the industry typically uses multi-tank ultrasonic cleaning machines, which utilize the cavitation effect induced by ultrasonic waves in the cleaning fluid to remove contaminants such as stretching oil and metal dust from the casing surface.

[0003] Currently, existing cleaning machine control systems mainly adopt two working modes: one is the open-loop fixed parameter mode, in which the ultrasonic generator operates at a fixed frequency and constant power, and cannot adjust parameters in real time according to changes in the load in the cleaning tank; the other is the total sound intensity feedback mode, in which only the total sound pressure level in the cleaning tank is monitored, and the output power of the ultrasonic generator is blindly increased when a decrease in sound pressure is detected.

[0004] However, the battery casing is a thin-walled, hollow structure. When the number of workpieces in the cleaning tank changes, or when the workpiece placement angle creates specific acoustic boundary conditions, the sound field distribution within the tank will change drastically. This leads to the following defects in the existing control system during actual operation: First, when the workpiece itself experiences a natural frequency drift, the ultrasonic frequency may resonate with the battery casing, causing instantaneous pitting or even perforation on the casing surface, resulting in irreversible physical damage. Second, the existing system cannot distinguish between effective cleaning cavitation and destructive cavitation. When the number of workpieces is small, excess energy may damage the workpieces, while insufficient energy may lead to residue, thus affecting the cleaning effect. These problems have become key factors restricting further improvement in the manufacturing yield of battery casings, and also pose potential hazards to the safety of power batteries. Summary of the Invention

[0005] To address the technical problem that existing cleaning machine control systems cannot adapt to changes in the sound field and distinguish between effective cavitation and destructive cavitation during ultrasonic cleaning of power battery casings, which can easily cause workpiece damage or incomplete cleaning, this invention provides a control system and control method for a battery casing cleaning machine.

[0006] In a first aspect, the present invention provides a control method for a battery casing cleaning machine, comprising: acquiring an original sound field signal in a cleaning tank; performing detrending term processing and high-pass filtering on the original sound field signal to obtain a preprocessed sound field signal; calculating the permutation entropy of the sound field signal to assess the load complexity in the current cleaning tank; adaptively adjusting the penalty factor of the variational mode decomposition algorithm based on the load complexity; decomposing the sound field signal using the adjusted penalty factor to extract a fundamental frequency mode representing cleaning force and a high-frequency mode representing cavitation impact; calculating the energy ratio between the high-frequency mode and the fundamental frequency mode; and constructing a cavitation erosion risk index for assessing the possibility of damage to the battery casing surface based on the time-domain fluctuation characteristics of the high-frequency mode; comparing the cavitation erosion risk index with a preset cavitation erosion safety threshold; calculating the risk excess based on the comparison result; and sequentially performing frequency jitter adjustment and power auxiliary adjustment based on the risk excess until the cavitation erosion risk index is lower than the safety threshold.

[0007] First, the original acoustic field signal of the cleaning tank is preprocessed with detrending term and high-pass filtering. The load complexity within the tank is evaluated using permutation entropy. Based on this, the penalty factor of the variational mode decomposition algorithm is adaptively adjusted to decompose the fundamental frequency mode representing cleaning force and the high-frequency mode representing cavitation impact. Then, a cavitation erosion risk index is constructed by combining the energy ratio of the two types of modes and the time-domain fluctuation characteristics of the high-frequency mode. The risk excess is determined based on the difference between this index and the safety threshold, and frequency jitter adjustment and power auxiliary adjustment are performed sequentially. This invention can sense changes in the acoustic field environment within the cleaning tank in real time. By adaptively adjusting the parameters of the feature extraction algorithm, it accurately captures minute damage signals hidden in the noise and performs fine-grained coordinated adjustment of frequency and power accordingly, thereby maintaining efficient cleaning while avoiding physical damage to the battery casing.

[0008] Preferably, the relationship between the penalty factor of the variational mode decomposition algorithm adaptively adjusted based on the load complexity is as follows:

[0009] In the formula, This is the optimal penalty factor for the variational mode decomposition algorithm calculated at the current moment; Basic penalty factor; To adjust the gain coefficient; The permutation entropy is calculated in real time; The reference entropy value is a constant; This is the operation for the natural logarithm.

[0010] Through this relationship, the battery casing cleaning machine control system can adaptively adjust the algorithm parameters according to the number of battery casings in the cleaning tank. Under complex load and strong noise interference conditions, it can automatically narrow the modal frequency band and accurately extract the center frequency characteristics, thereby solving the problem of inconsistent control performance of traditional control systems under different load conditions and effectively avoiding misjudgments caused by working condition adaptation deviations.

[0011] Preferably, the step of decomposing the sound field signal using the adjusted penalty factor to extract the fundamental frequency mode representing the cleaning force and the high frequency mode representing the cavitation impact includes: performing variational mode decomposition on the sound field signal using the calculated penalty factor, wherein the number of modes is set to a preset value; Calculate the center frequency of each intrinsic mode function after decomposition; select the component with the smallest deviation between the center frequency and the set working frequency of the ultrasonic generator as the fundamental frequency mode; select the component with the center frequency greater than the preset high frequency threshold as the high frequency mode.

[0012] Preferably, the relationship of the cavitation erosion risk index is as follows:

[0013] In the formula, The cavitation erosion risk index; The energy value of the high-frequency mode; This represents the energy value of the fundamental frequency mode. The envelope standard deviation of the high-frequency modes; This is the set voltage value for the ultrasonic generator; This is the sensitivity adjustment coefficient; It is an exponential function with the natural constant as its base.

[0014] This relationship introduces a volatility index correction mechanism. Compared with the traditional method of using only energy monitoring, this invention can accurately identify transient cavitation risks with low energy amplitude but violent fluctuations. Such risks are the main cause of micro-pitting corrosion on the surface of the battery casing. This mechanism can reduce the incidence of pitting defects on the workpiece surface.

[0015] Preferably, the cavitation erosion risk index is compared with a preset cavitation erosion safety threshold, and the risk excess is calculated based on the comparison result, including: determining whether the cavitation erosion risk index is greater than the preset cavitation erosion safety threshold; If it is greater than the preset cavitation erosion safety threshold, the difference between the cavitation erosion risk index and the preset cavitation erosion safety threshold is calculated as the risk excess. If the risk excess is not greater than the specified value, the risk excess will be set to zero, and the ultrasonic generator will be kept running at the center frequency.

[0016] Preferably, frequency jitter adjustment and power-assisted adjustment are performed sequentially based on the risk excess, including: calculating the frequency superposition amount output to the ultrasonic generator, the relationship of which is:

[0017] In the formula, This is the amount of frequency superposition output to the generator; Maximum sweep width limit; This is the response rate coefficient; For excess risk; The modulation frequency; It is a time variable; Pi; It is a cosine function.

[0018] By employing active frequency perturbation instead of direct power reduction, the standing wave field within the cleaning tank is physically disrupted. This invention can maintain high-power cleaning and ensure deep hole cleanliness while reducing damage to the battery casing surface, thus meeting the high-speed, high-quality production requirements of lithium battery manufacturing.

[0019] Preferably, the power-assisted adjustment includes: real-time monitoring of the frequency superposition amount and the risk excess amount; If the amplitude of the frequency superposition reaches the preset frequency sweep width ratio, and the duration of the risk excess is greater than zero exceeds the preset judgment time window, then a power adjustment command is generated. In response to a power adjustment command, the output power of the ultrasonic generator is reduced linearly until the risk excess is reduced to zero.

[0020] Preferably, the calculation of the permutation entropy adopts a multi-scale permutation entropy algorithm; the acquisition of the original sound field signal in the cleaning tank includes: acquiring the signal by using broadband piezoelectric hydrophones arranged at the geometric center of the inner wall and the diagonal of the bottom of the cleaning tank.

[0021] This invention employs a multi-scale permutation entropy algorithm to calculate permutation entropy, using the entropy value to characterize the degree of order and disorder in the sound field. By collecting the original sound field signal through broadband piezoelectric hydrophones placed at the geometric center of the inner wall and the diagonal of the bottom of the cleaning tank, the sound field characteristic information can be accurately obtained, enabling a reliable assessment of the sound field state within the cleaning tank and providing an accurate data basis for subsequent adaptive parameter adjustment.

[0022] Preferably, the preprocessed sound field signal is a digital signal obtained by extracting a discrete time sequence with a preset time window, removing the drift DC component and filtering out the frequency band of water pump mechanical vibration.

[0023] Secondly, the present invention provides a control system for a battery casing cleaning machine, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned control method for a battery casing cleaning machine is implemented.

[0024] By adopting the above technical solution, the control method of the battery casing cleaning machine is generated into a computer program and stored in a memory so that it can be loaded and executed by a processor. A terminal device can then be made based on the memory and the processor for convenient use.

[0025] The technical solution of the present invention has the following beneficial technical effects: By characterizing the complexity of the sound field through multi-scale permutation entropy and dynamically adjusting the penalty factor of the variational mode decomposition algorithm, the characteristics of effective cavitation and destructive cavitation under strong noise are decoupled. By constructing a cavitation erosion risk model that integrates time-domain fluctuation characteristics, the sensitivity of identification and early warning of transient cavitation corrosion can be improved, avoiding damage to the workpiece during cleaning. At the same time, a coordinated control strategy of frequency perturbation and power-assisted adjustment is adopted to effectively suppress destructive cavitation and resonance damage while ensuring high cleaning efficiency, achieving a balance between cleaning effect and workpiece safety, and significantly improving the cleaning quality of power battery casings and the yield rate of workpieces. Attached Figure Description

[0026] Figure 1 This is a flowchart of a control method for a battery casing cleaning machine according to the present invention; Figure 2 It is a spatial distribution map of acoustic features decomposed based on the adaptive variational mode decomposition algorithm; Figure 3 This is a comparison chart of the distribution of cleaning quality and surface integrity of batch workpieces; Figure 4 It is a trajectory diagram for frequency and power dual-variable collaborative control optimization. Detailed Implementation

[0027] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0028] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0029] This invention discloses a control method for a battery casing cleaning machine, referring to... Figure 1 This includes steps S1-S4: S1. Obtain the original sound field signal from the cleaning tank, perform detrending processing and high-pass filtering on the original sound field signal to obtain the preprocessed sound field signal. The specific implementation is as follows: Signal Acquisition Setup: To accurately capture the sound field changes during the battery casing cleaning process, high-fidelity signal acquisition is required first. Two broadband piezoelectric hydrophones are installed at the geometric center of the inner wall and the diagonal of the bottom of the cleaning tank. For example, B&K8103 or other industrial-grade hydrophones can be used, with a frequency response range covering 10Hz to 200kHz, to capture the fundamental wave, harmonics, and high-frequency broadband noise of the ultrasonic waves. The sampling frequency of the data acquisition card is then set. The sampling frequency is 500kHz, set according to the Nyquist sampling theorem, which covers more than 10 times the normal operating frequency of the cleaning machine, ensuring that high-frequency transient signals are not distorted. The temperature drift DC component refers to the DC bias signal generated by the hydrophone due to temperature changes within the cleaning tank. This component is not part of the effective sound field signal and should be eliminated during the preprocessing stage.

[0030] Signal interception and preprocessing: The original sound field signal is truncated within a time window to obtain a discrete time series. To address the DC component of temperature drift in the sensor signal and the low-frequency mechanical vibration interference below 5kHz generated by the water pump, [further measures are needed]. Detrending processing is performed to eliminate DC drift, followed by high-pass filtering with a cutoff frequency of 5kHz to obtain the pre-processed, clean sound field signal. .

[0031] By employing high-fidelity signal acquisition methods and targeted preprocessing operations, interferences such as DC drift and low-frequency mechanical vibration, which are unrelated to cavitation effects, were eliminated, resulting in a pure sound field signal containing complete high-frequency characteristics. This laid a solid data foundation for subsequent acoustic feature extraction.

[0032] S2. Calculate the multi-scale permutation entropy of the sound field signal to evaluate the load complexity in the cleaning tank. Based on the load complexity, adaptively adjust the penalty factor of the variational mode decomposition algorithm. Use the adjusted penalty factor to decompose the sound field signal and extract the fundamental frequency mode representing the cleaning force and the high-frequency mode representing the cavitation impact.

[0033] The variational mode decomposition algorithm in this step is a tool for decomposing complex signals into several intrinsic mode functions. In the scenario of battery casing cleaning, the greater the load in the cleaning tank, i.e., the more battery casings there are, the more complex the reflection and scattering of sound waves become. If the penalty factor of the variational mode decomposition algorithm is not adjusted accordingly, mode aliasing can easily occur, causing the characteristic signals representing workpiece damage to be submerged by noise.

[0034] Calculate the preprocessed sound field signal Multiscale permutation entropy ; The magnitude of the value is positively correlated with the degree of disorder in the sound field within the cleaning tank. That is, the larger the entropy value of the multi-scale arrangement, the more load there is in the cleaning tank and the more chaotic the sound wave reflection and scattering. The smaller the entropy value of the multi-scale arrangement, the less load there is in the cleaning tank and the more orderly the sound field.

[0035] Based on real-time calculated multi-scale permutation entropy, the optimal penalty factor for the variational mode decomposition algorithm is adaptively constructed using the following relation. :

[0036] In the formula, The basic penalty factor is taken as an empirical constant of 2000, which is applicable to the decomposition of standard single-frequency ultrasonic signals. To adjust the gain coefficient, the value ranges from 0.5 to 1, which is used to adjust the algorithm's sensitivity to load changes; For real-time calculation of multi-size permutation entropy; The average entropy value of the cleaning tank during empty operation is taken as the reference entropy constant. This is the operation for the natural logarithm.

[0037] Optimal penalty factor With multi-scale permutation entropy An adaptive linkage mechanism based on logarithmic mapping was established. The physical mechanism lies in the fact that the nonlinear collapse of cavitation bubbles and the impact of microjets within the ultrasonic cleaning tank induce strong chaotic characteristics in the sound field. Through logarithmic function mapping, the multi-scale permutation entropy, which characterizes the degree of chaos in this sound field, can be transformed into a penalty factor controlling the frequency band resolution in the variational mode decomposition algorithm, thus achieving adaptive parameter optimization.

[0038] To facilitate understanding, we will now further explain it with specific numerical examples: Setting the base penalty factor for Adjusting the gain coefficient It is 0.8, referring to the entropy value. The value is 1, and the calculation is performed under the following two working conditions: Condition 1: The cleaning tank is empty or has very little load, the sound field is ordered, and the measured... Substituting 1 into the relation, we get:

[0039] Condition 2: The cleaning tank is fully loaded, and the sound field reflection is chaotic. Measurements were taken as follows: Substituting 2 into the relation, we get:

[0040] Punishment factor The value is negatively correlated with the modal bandwidth decomposed by the variational mode decomposition algorithm, i.e., the multi-scale permutation entropy. Increasing the penalty factor As the frequency band increases, the modal bandwidth narrows, enabling the system to extract only the center frequency component with the most concentrated energy in a strong noise environment. This avoids misjudging broadband noise as a characteristic signal, thus achieving an adaptive decomposition effect that becomes more focused the more chaotic the environment.

[0041] The penalty factor obtained through adaptive calculation is substituted into the variational mode decomposition algorithm to set the number of modes. The value is 4, for the sound field signal Perform variational mode decomposition; extract the fundamental frequency mode with a center frequency closest to 28kHz from the obtained intrinsic mode functions. and high-frequency modes with frequencies greater than 100kHz Among them, the fundamental frequency mode Represents the effective cleaning power of ultrasound, high-frequency mode This represents the destructive shock wave generated by cavitation.

[0042] By introducing multi-scale permutation entropy to characterize the load complexity in the cleaning tank, the penalty factor in the variational mode decomposition algorithm is adaptively adjusted, effectively solving the mode aliasing problem under different load conditions during battery casing cleaning. It can accurately separate the fundamental frequency component related to the cleaning effect and the high-frequency impact component related to workpiece damage, providing a reliable characteristic mode basis for subsequent cavitation erosion risk assessment.

[0043] S3. Calculate the energy ratio of the high-frequency mode to the fundamental frequency mode, and combine the time-domain fluctuation characteristics of the high-frequency mode to construct a cavitation erosion risk index for assessing the possibility of damage to the battery casing surface.

[0044] Specifically, cavitation erosion risk index The relation is:

[0045] In the formula, is the cavitation erosion risk index, which is a dimensionless parameter; The energy value of the high-frequency mode is the sum of the squares of the discrete point amplitudes. The energy value of the fundamental frequency mode is the sum of the squares of the discrete amplitudes; the 1 in the denominator of the formula represents the unit and the fundamental frequency mode. A consistent energy reference constant is used to prevent the denominator from being zero; The envelope standard deviation is the high-frequency mode, and this parameter reflects the time-domain instability of cavitation shock. This is the set voltage value for the ultrasonic generator; This is the sensitivity adjustment coefficient, with a value ranging from 2 to 5; This is a natural exponentiation operation. It should be noted that, to comply with mathematical and physical operational standards, the energy value of the fundamental mode in the above formula is... Energy value of high-frequency modes , envelope standard deviation of high-frequency modes and the set voltage value of the ultrasonic generator Variables with physical dimensions have been pre-divided by their respective unit base values ​​to become dimensionless before being substituted into the relational calculation, and participate in addition and exponential mapping operations in pure numerical form.

[0046] The cavitation erosion risk index is constructed by drawing on the physical model of cavitation damage accumulation in fluid mechanics. The energy ratio of the high-frequency mode to the fundamental mode directly reflects the energy competition between transient and steady-state cavitation. The exponential amplification term based on the standard deviation accurately fits the physical law that the nonlinear destructive force on the surface of the solid workpiece increases exponentially when the cavitation cloud collapses and causes violent fluctuations in the sound field.

[0047] To facilitate understanding, we will now further explain it with specific numerical examples: Setting the set voltage of the ultrasonic generator. Sensitivity adjustment coefficient The calculation is performed under two different working conditions: Operating Condition 1: The cavitation effect is stable and the impact fluctuations are small. ; Index term: Risk Index .

[0048] Operating Condition 2: Cavitation effects are extremely unstable and exhibit transient impacts, as measured... ; Index term: Risk Index .

[0049] The above calculation results show that when cavitation impact exhibits significant temporal instability fluctuations, the risk index nearly doubles. The introduction of the natural exponential function amplifies the impact of this fluctuation on risk assessment, enabling the system to provide early warning before substantial cavitation erosion damage occurs in the battery casing.

[0050] By constructing a risk assessment model, the instability of cavitation impact is incorporated into the assessment of cavitation erosion risk of battery casing. This model can sensitively identify potential pitting erosion risks that have not reached extreme energy levels but have fluctuated violently. It overcomes the lag and omission problems of traditional methods that rely solely on energy thresholds to judge cavitation erosion, and improves the sensitivity and accuracy of risk monitoring.

[0051] S4. Compare the cavitation erosion risk index with the preset cavitation erosion safety threshold, calculate the risk excess based on the comparison result, and perform frequency jitter adjustment and power auxiliary adjustment in sequence based on the risk excess until the cavitation erosion risk index is lower than the safety threshold.

[0052] Specifically, the safety threshold for cavitation erosion is set as follows: The real-time monitored cavitation erosion risk index is compared with the preset cavitation erosion safety threshold: If Then the excess risk ;like Then set The system maintains a routine cleaning state. The cavitation erosion safety threshold is determined experimentally by measuring the critical damage point of the battery casing to be cleaned.

[0053] Based on risk excess The frequency superposition amount output to the ultrasonic generator is calculated using the following formula. To achieve jitter adjustment of the output frequency of the cleaning center:

[0054] In the formula, Maximum sweep width limit; This is the response rate coefficient; For excess risk; The modulation frequency; It is a time variable; Pi; The frequency superposition is a cosine function. A control strategy of bounded exponential decay function and triangular carrier wave co-modulation is employed. This strategy aims to construct a continuous and smooth frequency disturbance trajectory, dynamically disrupting the steady-state acoustic field distribution within the cleaning tank without triggering large fluctuations in output power, thereby suppressing local cavitation overload and pitting damage to the workpiece caused by standing wave effects.

[0055] To facilitate understanding, we will now further explain it with specific numerical examples: Assume 500Hz =1, 20Hz: like ,but The frequency superposition amount output to the ultrasonic generator The ultrasonic generator maintains a fixed center frequency, ensuring stable cleaning of the system; like ,but The frequency superposition amount output to the ultrasonic generator. .

[0056] At this time, the ultrasonic generator superimposes a reciprocating sweep frequency signal with an amplitude of 195Hz on the basic center frequency, which can disrupt the steady-state standing wave field in the cleaning tank, causing the energy accumulation point to move in space, thus avoiding damage such as overheating and cavitation erosion caused by single-point energy accumulation on the surface of the battery casing.

[0057] Because frequency jitter adjustment has a higher trigger priority than power auxiliary adjustment; if the frequency superposition amount The amplitude is close to the maximum sweep width. Furthermore, the risk exceeds the limit after this state lasts for 0.5 seconds. If the value is still greater than 0, it indicates that the risk of cavitation erosion cannot be eliminated by frequency perturbation alone. This situation is often caused by insufficient load in the cleaning tank, resulting in excess total energy. At this time, the controller generates a power adjustment command to the ultrasonic generator, reducing the output power linearly until the risk exceeds the limit. The risk index for zeroing out and cavitation erosion is below the safety threshold. It is worth noting that frequency perturbation refers to a control method that superimposes small, periodic frequency fluctuations onto the fundamental operating center frequency of the ultrasonic generator.

[0058] A coordinated control strategy of prioritizing frequency perturbation and assisting with power regulation is adopted. Prioritizing the elimination of energy accumulation hotspots by disrupting the steady-state standing wave field, and only adjusting the power when frequency regulation is ineffective, thereby preserving the ultrasonic cleaning capability to the maximum extent while actively protecting the battery casing workpiece.

[0059] Reference Figure 2 The figure shows the distribution characteristics of samples under normal cleaning and samples at risk of cavitation erosion. It can be seen that after processing by the adaptive variational mode decomposition algorithm of the present invention, the two types of samples are clearly separated by the safe operation boundary and the high-risk warning zone boundary, which verifies that the method of the present invention has a strong ability to distinguish acoustic features.

[0060] Reference Figure 3 The detection data points using the adaptive collaborative control method of this invention exhibit a highly concentrated distribution characteristic, and all fall within the high-quality product index range. Compared with the discrete data distribution of the fixed parameter control in the prior art, this invention clearly demonstrates its advantage in improving the yield of battery casing cleaning.

[0061] Reference Figure 4 The vertical axis of the graph represents the percentage of ultrasonic output power, indicating the amount of power-assisted adjustment; the horizontal axis represents the ultrasonic frequency fine-tuning offset, indicating the amount of frequency perturbation adjustment; the color scale represents the cavitation erosion risk index; the trajectory starts from the initial state point in the high-risk resonance zone. After the controller intervenes, the trajectory extends to the right along the direction of frequency perturbation, accompanied by a small downward offset of power fine-tuning, quickly avoiding the resonance risk zone, and finally stabilizing at the steady-state control point in the safe cleaning zone, fully verifying the effectiveness of the frequency and power coordinated control strategy of this invention.

[0062] This invention also discloses a control system for a battery casing cleaning machine, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a control method for a battery casing cleaning machine according to the present invention is implemented.

[0063] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0064] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A control method for a battery casing cleaning machine, characterized in that, include: The original sound field signal in the cleaning tank is acquired, and the original sound field signal is processed by detrending term processing and high-pass filtering to obtain the preprocessed sound field signal. The arrangement entropy of the sound field signal is calculated to evaluate the load complexity in the current cleaning tank. Based on the load complexity, the penalty factor of the variational mode decomposition algorithm is adaptively adjusted. The sound field signal is decomposed using the adjusted penalty factor to extract the fundamental frequency mode representing the cleaning force and the high frequency mode representing the cavitation impact. Calculate the energy ratio of the high-frequency mode to the fundamental frequency mode, and combine the time-domain fluctuation characteristics of the high-frequency mode to construct a cavitation erosion risk index for assessing the possibility of damage to the battery casing surface; The cavitation erosion risk index is compared with a preset cavitation erosion safety threshold. The risk excess is calculated based on the comparison result. Frequency jitter adjustment and power auxiliary adjustment are performed sequentially based on the risk excess until the cavitation erosion risk index is lower than the safety threshold.

2. The control method for a battery casing cleaning machine according to claim 1, characterized in that, The relationship between the penalty factor of the variational mode decomposition algorithm adaptively adjusted based on the load complexity is as follows: In the formula, This is the optimal penalty factor for the variational mode decomposition algorithm calculated at the current moment; Basic penalty factor; To adjust the gain coefficient; The permutation entropy is calculated in real time; The reference entropy value is a constant; This is the operation for the natural logarithm.

3. The control method for a battery casing cleaning machine according to claim 1, characterized in that, The step of decomposing the sound field signal using the adjusted penalty factor to extract the fundamental frequency mode representing the cleaning force and the high frequency mode representing the cavitation impact includes: performing variational mode decomposition on the sound field signal using the calculated penalty factor, wherein the number of modes is set to a preset value. Calculate the center frequency of each intrinsic mode function after decomposition; select the component with the smallest deviation between the center frequency and the set working frequency of the ultrasonic generator as the fundamental frequency mode; select the component with the center frequency greater than the preset high frequency threshold as the high frequency mode.

4. The control method for a battery casing cleaning machine according to claim 1, characterized in that, The relationship for the cavitation erosion risk index is as follows: In the formula, The cavitation erosion risk index; The energy value of the high-frequency mode; This represents the energy value of the fundamental frequency mode. The envelope standard deviation of the high-frequency modes; This is the set voltage value for the ultrasonic generator; This is the sensitivity adjustment coefficient; It is an exponential function with the natural constant as its base.

5. The control method for a battery casing cleaning machine according to claim 1, characterized in that, The cavitation erosion risk index is compared with a preset cavitation erosion safety threshold, and the risk excess is calculated based on the comparison result, including: determining whether the cavitation erosion risk index is greater than the preset cavitation erosion safety threshold. If it is greater than the preset cavitation erosion safety threshold, the difference between the cavitation erosion risk index and the preset cavitation erosion safety threshold is calculated as the risk excess. If the risk is not greater than the specified value, the excess risk will be set to zero, and the ultrasonic generator will be kept running at the center frequency.

6. The control method for a battery casing cleaning machine according to claim 5, characterized in that, Based on the aforementioned risk excess, frequency jitter adjustment and power auxiliary adjustment are performed sequentially, including: calculating the frequency superposition amount output to the ultrasonic generator, the relationship of which is: In the formula, This is the amount of frequency superposition output to the generator; Maximum sweep width limit; This is the response rate coefficient; For excess risk; The modulation frequency; It is a time variable; Pi; It is a cosine function.

7. The control method for a battery casing cleaning machine according to claim 6, characterized in that, The power-assisted adjustment includes: real-time monitoring of the frequency superposition amount and the risk excess amount; If the amplitude of the frequency superposition reaches the preset frequency sweep width ratio, and the duration of the risk excess is greater than zero exceeds the preset judgment time window, then a power adjustment command is generated. In response to a power adjustment command, the output power of the ultrasonic generator is reduced linearly until the risk excess is reduced to zero.

8. The control method for a battery casing cleaning machine according to claim 1, characterized in that, The calculation of the permutation entropy adopts a multi-scale permutation entropy algorithm; the acquisition of the original sound field signal in the cleaning tank includes: acquiring the signal by using broadband piezoelectric hydrophones arranged at the geometric center of the inner wall and the diagonal of the bottom of the cleaning tank.

9. The control method for a battery casing cleaning machine according to claim 1, characterized in that, The preprocessed sound field signal is a digital signal obtained by extracting a discrete time sequence with a preset time window, removing the drift DC component and filtering out the frequency band of water pump mechanical vibration.

10. A control system for a battery casing cleaning machine, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement the control method for the battery casing cleaning machine according to any one of claims 1-9.

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