A method for noise induction and suppression of an energy storage device

Abstract

The present invention relates to the field of energy storage technologies, and specifically relates to a method for noise induction and suppression of an energy storage device, including: recording the energy storage control characteristics and the corresponding device noise when the energy storage device is operating, and for the device noise corresponding to each energy storage control characteristic, the noise response ratio of the N noise components with the strongest noise response in the device noise is recorded as the noise suppression ratio of each energy storage control characteristic; N is the number of anti-phase sound sources; for the difference A between the device noise corresponding to any energy storage control characteristic and the current moment noise, and the difference B between any energy storage control characteristic and the current moment energy storage control characteristic, a predicted control characteristic is obtained according to A and B; the predicted control characteristic is used to further regulate and intervene in the working process of the energy storage device, and then noise suppression is performed using the anti-phase sound source. The present invention further improves the noise suppression effect.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage, and particularly relates to a method for noise induction and suppression of an energy storage device. Background Art

[0002] Energy storage devices such as lithium-ion batteries, supercapacitors, flywheel energy storage, and compressed air energy storage (CAES) are widely used in scenarios such as peak shaving and frequency modulation of power systems, grid connection of new energy, and improvement of grid stability. However, these energy storage devices will generate significant noise problems during operation, mainly from electromagnetic interference of power electronic devices, vibration of mechanical components, and operation of the heat dissipation system.

[0003] Currently, the noise suppression technologies for energy storage devices are mainly divided into two categories - passive sound insulation methods and active noise control methods. Among them, sound insulation enclosures and reverse sound source cancellation are the most commonly used technical means: The sound insulation enclosure method controls noise within a closed or semi-closed space by installing sound insulation covers, sound insulation panels, or sound insulation walls around the energy storage device to reduce the propagation and diffusion of noise. Sound insulation materials usually use multi-layer structures, including sound-absorbing cotton, damping layers, and sound insulation panels. The overall structural design considers the reflection, absorption, and attenuation performance of sound waves. This method not only has high costs and large space occupation, but also easily leads to difficult heat dissipation of the device, affecting the stable operation of the energy storage device, and at the same time, the noise suppression effect is limited.

[0004] The reverse sound source cancellation (ANC, Active Noise Control) method (i.e., the active noise control method) arranges speakers (called anti-sound source devices) on the noise propagation path to generate reverse sound waves with opposite phases to the noise in real time to cancel the noise energy mutually. This method has limited suppression effects on complex sound fields (such as multi-source noise or non-steady noise) and is difficult to completely eliminate noise.

[0005] In summary, the existing noise suppression methods for energy storage devices have insufficient noise suppression effects. Summary of the Invention

[0006] To solve the above problems, the present invention provides a method for noise induction and suppression of an energy storage device.

[0007] A method for noise induction and suppression of an energy storage device of the present invention adopts the following technical solutions:

[0008] An embodiment of the present invention provides a method for noise induction and suppression of an energy storage device, and the method includes the following steps:

[0009] Record the energy storage control characteristics and the corresponding device noise when the energy storage device is working, and the energy storage control characteristics at least include the charge and discharge rate of the energy storage device;

[0010] Among the device noises corresponding to each energy storage control feature, the proportion of the noise responses of the N noise components with the strongest noise responses is recorded as the noise suppression ratio of each energy storage control feature; N is the number of anti-phase sound sources; the anti-phase sound source refers to a device that can generate a specified sound wave;

[0011] For the difference A between the device noise corresponding to any energy storage control feature and the noise at the current moment, and the difference B between any energy storage control feature and the energy storage control feature at the current moment, the energy storage control feature when the difference between A and f×B is the largest is used as the predicted control feature; f is recorded as the similarity suppression coefficient corresponding to the predicted control feature, and the initial value of f is a preset value;

[0012] Obtain the noise suppression ratios of the predicted control features obtained when the similarity suppression coefficient takes different values, and record the predicted control feature with the largest noise suppression ratio and similarity suppression coefficient as the target control feature;

[0013] The energy storage device operates with the target control feature at the next moment, and records the device noise D0 collected in real time. The sound wave generated by the anti-phase sound source has the same amplitude and frequency and opposite phase as the N noise components with the strongest noise responses in D0.

[0014] Preferably, the step of obtaining the noise suppression ratios of the predicted control features obtained when the similarity suppression coefficient takes different values, and recording the predicted control feature with the largest noise suppression ratio and similarity suppression coefficient as the target control feature includes the following specific steps:

[0015] Preset the value range of the similarity suppression coefficient. For any similarity suppression coefficient f1 within the value range, the predicted control feature corresponding to f1 is recorded as F1, the noise suppression ratio of F1 is recorded as q1, record q1 + f1 as the evaluation index of f1, and record the f1 with the largest evaluation index as fm. The predicted control feature corresponding to fm is recorded as the target control feature.

[0016] Another embodiment of the present invention provides a method for suppressing the noise of an energy storage device based on feedback, and the method includes:

[0017] Record the energy storage control features and the corresponding device noises when the energy storage device operates. The energy storage control features at least include the charge and discharge rates of the energy storage device;

[0018] Among the device noises corresponding to each energy storage control feature, the proportion of the noise responses of the N noise components with the strongest noise responses is recorded as the noise suppression ratio of each energy storage control feature; N is the number of anti-phase sound sources; the anti-phase sound source refers to a device that can generate a specified sound wave;

[0019] For the difference A between the device noise corresponding to any energy storage control feature and the noise at the current moment, and the difference B between any energy storage control feature and the energy storage control feature at the current moment, the energy storage control feature when the difference between A and B is the largest is used as the predictive control feature;

[0020] S1: The energy storage device operates with the predictive control feature at the next moment, and the device noise D0 is collected; the sound waves generated by the anti-sound source and the N noise components with the strongest noise response in D0 have the same amplitude and frequency and opposite phases;

[0021] S2: Obtain the noise suppression error w of the predictive control feature by taking the difference between the maximum value max of the noise suppression ratio of the energy storage control feature and the noise suppression ratio corresponding to D0, and obtain the similarity suppression coefficient f of the predictive control feature, and f is negatively correlated with w;

[0022] S3: Obtain the predictive control feature again. When obtaining the predictive control feature again, the energy storage control feature when the difference between A and f×B is the largest is used as the predictive control feature;

[0023] S4: Suppress the device noise of the energy storage device by repeatedly executing S1, S2, and S3.

[0024] Preferably, for the device noise corresponding to each energy storage control feature, the specific steps for obtaining the N noise components with the strongest noise response are as follows:

[0025] Use the Fourier transform algorithm to decompose the device noise under each energy storage control feature into several noise components, take the amplitude of the noise component as the noise response of the noise component, and the N noise components with the largest noise response are used as the N noise components with the strongest noise response.

[0026] Preferably, the specific steps for obtaining the noise response ratio are as follows:

[0027] The N noise components with the strongest noise response are denoted as the first components; the ratio of the mean value of the noise responses of the first components to the mean value of the noise responses of all the noise components of the device noise is denoted as the noise response ratio.

[0028] Preferably, the specific steps for obtaining the difference A between the device noise corresponding to any energy storage control feature and the noise at the current moment are as follows:

[0029] The noise at the current moment is the device noise collected at the current moment during the operation of the energy storage device, and the device noise is expressed as a time series; obtain the first difference between the average signal values of the time series corresponding to the device noise and the noise at the current moment, and obtain the DTW distance between the time series corresponding to the device noise and the noise at the current moment;

[0030] A is positively correlated with the first difference and the DTW distance respectively.

[0031] Preferably, the specific steps for obtaining the difference B between any energy storage control feature and the current - moment energy storage control feature are as follows:

[0032] The current - moment energy storage control feature represents the energy storage control feature at the current moment during the operation of the energy storage device, and the energy storage control feature is represented as a vector;

[0033] The Euclidean distance between the energy storage control feature and the vector corresponding to the current - moment energy storage control feature is taken as the value of B.

[0034] Preferably, the specific steps for obtaining the maximum value max of the noise suppression ratio of the energy storage control feature are as follows:

[0035] Perform mean - shift clustering on the noise suppression ratios of all energy storage control features to obtain all categories, calculate the mean value of all noise suppression ratios within each category, obtain the category with the smallest difference between the mean value of all noise suppression ratios and F0, and denote the maximum value of the noise suppression ratio in this category as max; F0 represents the noise suppression ratio corresponding to D0.

[0036] Preferably, the noise suppression error w=(max - F0) / max.

[0037] Preferably, the similarity suppression coefficient f = 1 - w.

[0038] The beneficial effects of the technical solution of the present invention are:

[0039] In an embodiment of the present invention, the energy storage control feature with the largest difference when A and f×B are used is used as the predictive control feature, the noise suppression ratios of the predictive control features obtained with different values of the similarity suppression coefficient are obtained, and the predictive control feature with the largest noise suppression ratio and similarity suppression coefficient is denoted as the target control feature; the energy storage device operates and suppresses noise with the target control feature at the next moment. This process first appropriately regulates the operation process of the energy storage device, so that the device noise generated by the regulated energy storage device is reduced as much as possible, and at the same time, the reduced device noise can be suppressed as much as possible by the anti - sound source. By dynamically suppressing noise from both the control of the energy storage device and the control of the anti - sound source, the noise suppression effect is improved; in addition, noise can be suppressed to a large extent each time an adjustment is made.

[0040] In another embodiment of the present invention, the energy storage control characteristics when the difference between A and B is the largest are used as the predictive control characteristics, and the energy storage device is adjusted and noise suppressed based on the predictive control characteristics. The predictive control characteristics are dynamically updated by the noise suppression error during the next noise suppression. This process also realizes the proper regulation of the working process of the energy storage device, so that the device noise generated by the regulated energy storage device is minimized as much as possible, and at the same time, the reduced device noise can be suppressed as much as possible by the anti-noise source. By dynamically suppressing the noise from both the control of the energy storage device and the control of the anti-noise source, the noise suppression effect is improved; in addition, it can ensure a fast response and a good overall suppression effect during the entire noise suppression process.

[0041] Generally speaking, the present invention further improves the noise suppression effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0043] Figure 1 It is a flowchart of the steps of a method for noise induction and suppression of an energy storage device provided by an embodiment of the present invention;

[0044] Figure 2 It is a flowchart of the steps of a method for noise suppression of an energy storage device based on feedback provided by another embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, describe in detail the specific implementation manner, structure, characteristics and effects of a method for noise induction and suppression of an energy storage device proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0047] The following will specifically describe the specific solution of a method for noise induction and suppression of an energy storage device provided by the present invention in conjunction with the accompanying drawings.

[0048] Embodiment 1:

[0049] Please refer to Figure 1 , which shows a flowchart of the steps of a method for noise induction and suppression of an energy storage device provided by an embodiment of the present invention. The method includes the following steps:

[0050] Step S101: Record the energy storage control characteristics and the corresponding device noise when the energy storage device is working. The energy storage control characteristics at least include the charge and discharge rate of the energy storage device.

[0051] During the operation of an energy storage device (such as a lithium battery, a flywheel energy storage device, a compressed air energy storage device, etc.), certain device noise will be generated. The sources of these noises include but are not limited to the following aspects: (1) Mechanical vibration noise: Thermal expansion / contraction during battery charging and discharging, rotating components of flywheel energy storage, compressors of compressed air energy storage, etc. Long-term vibration may cause structural fatigue and generate low-frequency noise pollution. (2) Electromagnetic noise: High-frequency switching actions during the operation of power electronic devices (such as inverters and converters) generate high-frequency noise pollution. (3) Cooling system noise: Medium and high-frequency noise generated during the operation of cooling fans, water pumps, etc.

[0052] Existing noise suppression methods usually cover sound insulation materials, enclose sound insulation panels, etc. to reduce the impact of noise. However, on the one hand, these methods are costly, and on the other hand, they cannot completely suppress all noises. All embodiments of the present invention are implemented on the basis of existing noise suppression methods, so that the device noise is further suppressed.

[0053] In this embodiment, a microphone is placed around each energy storage device to collect and record the device noise at each moment when the energy storage device is working in real time.

[0054] Furthermore, during the operation of the energy storage device, its charge and discharge process is regulated by an energy storage control system. The energy storage control system is a subsystem in the power grid system and is a well-known technology. The specific working principle of the energy storage control system will not be described in this embodiment. When the energy storage control system regulates the energy storage device, it calculates the charge and discharge rate for regulating the energy storage device in real time, controls the switching of the charging state and discharging state of the energy storage device, etc., so that the energy storage device can maintain the safe and stable operation of the power grid (for example, maintaining the stability of the power grid through the peak shaving and valley filling function of the energy storage device).

[0055] In this embodiment, the charge and discharge rate when the energy storage device is in the charge and discharge state under the mediation of the energy storage control system is used as the energy storage control characteristic when the energy storage device is working.

[0056] In some embodiments, the energy storage control system can not only adjust the charge and discharge rate of the energy storage device, but also adjust the SOC (State of Charge) target value, DOD (Depth of Discharge) limit, target terminal voltage or output voltage of the energy storage device, etc. Therefore, in other embodiments, these values can also be used as part of the energy storage control characteristics. In other embodiments, the energy storage control characteristics can be set according to specific energy storage devices, and no specific limitation is made in this embodiment.

[0057] Furthermore, considering that when the energy storage control system adjusts the operation of the energy storage device, different degrees of noise interference will be generated under different energy storage control characteristics of the energy storage device. For example, the change in the magnitude of the charge and discharge rate will change the switching actions during the operation of power electronic devices (such as inverters and converters), thereby generating different noise interferences. In this embodiment, the noise interference is reduced by regulating the energy storage control characteristics of the energy storage device. Before that, this embodiment first obtains and records the energy storage control characteristics of the energy storage device at each moment during the historical operation process; note that the energy storage control characteristics recorded in this embodiment refer to the energy storage control characteristics calculated or allocated by the energy storage control system during the operation of the energy storage device, and this energy storage control characteristic is determined by the power grid operation environment where the energy storage device is located (the energy storage control characteristics adjusted in this embodiment will not be recorded later).

[0058] So far, this embodiment has obtained the energy storage control characteristics of the energy storage device at each moment during the historical operation process, and the device noise corresponding to each energy storage control characteristic. The historical operation process described in this embodiment refers to the operation process in the most recent month, and in other embodiments, it can refer to the operation process in the most recent quarter. No specific limitation is made in this embodiment.

[0059] As an example, the specific method for obtaining the device noise at each moment includes:

[0060] In this embodiment, every two seconds is regarded as a moment (in other embodiments, it can be every 5 seconds). At any time point during the historical operation process of the energy storage device, the signal time sequence output by the microphone within the time period T before this time point is obtained. This signal time sequence output by the microphone is used as the device noise at each moment. The microphone used in this embodiment outputs a signal every 0.1 second (the voltage signal is collected in this embodiment).

[0061] In this embodiment, the time period T refers to the time period composed of 10 moments (including this time point) before this time point. In other embodiments, the time period T can be set to other values, and no specific limitation is made in this embodiment.

[0062] Step S102: Among the device noises corresponding to each energy storage control feature, the proportion of the noise responses of the N noise components with the strongest noise responses is recorded as the noise suppression ratio of each energy storage control feature; N is the number of anti-sound sources.

[0063] Using anti-sound sources for noise suppression is a commonly used method. This method makes the anti-sound sources generate a sound wave signal with the same amplitude and opposite phase as the device noise to cancel the device noise and thus achieve the effect of noise suppression; this method is applied in fields such as automobiles and industrial pipelines. It should be noted that the anti-sound sources described in this embodiment are essentially a sound wave generating device that can generate sound waves with the required amplitude, frequency, and phase. This device is a well-known technology and is not specifically limited in this embodiment.

[0064] However, the noise signal components contained in the device noise are numerous and dynamically changing. When multiple conventional anti-sound sources work with a fixed phase, it is difficult to achieve a good noise suppression effect.

[0065] The noise suppression ratio of each energy storage control feature in this embodiment is used to describe the N noise components with the strongest noise responses in the device noise under each energy storage control feature. The larger the proportion of the noise responses (i.e., the noise suppression ratio) of these N noise components, the more it indicates that when these N noise components are suppressed (for example, after being suppressed using N anti-sound sources), the influence of the device noise at each moment (or under each energy storage control feature) can be significantly reduced.

[0066] As an example, the specific steps for obtaining the N noise components with the strongest noise responses in the device noise under each energy storage control feature are as follows:

[0067] For the device noise under each energy storage control feature, use the Fourier transform algorithm to decompose the device noise into several noise components, and each noise component has a single frequency and amplitude;

[0068] Take the amplitude value of the noise component as the noise response of the noise component, and the N noise components with the largest noise responses are used as the N noise components with the strongest noise responses.

[0069] In this example, the magnitude of the amplitude is used to measure the strength of the noise response, that is, the louder the sound, the stronger the noise response.

[0070] As another example, the specific steps for obtaining the N noise components with the strongest noise responses in the device noise under each energy storage control feature are as follows:

[0071] Take the product of the amplitude value of the noise component and the frequency as the noise response of the noise component, and the N noise components with the largest noise responses are used as the N noise components with the strongest noise responses.

[0072] In this example, the strength of the noise response is measured by both the amplitude and the frequency, that is, the louder and harsher the sound, the stronger the noise response.

[0073] As an example, for the equipment noise under each energy storage control feature, the specific method for obtaining the proportion of the noise response of the N noise components with the strongest noise response is as follows:

[0074] The N noise components with the strongest noise response are denoted as the first components; the mean value of the noise responses of all the first components is obtained and denoted as the first mean value. For all the noise components of the equipment noise, the mean value of the noise responses of these noise components is denoted as the second mean value, and the ratio of the first mean value to the second mean value is denoted as the proportion of the noise response.

[0075] In this embodiment, the proportion of the noise response focuses on describing the proportion of the noise response of the noise components with relatively strong noise responses in the overall noise response.

[0076] As another example, for the equipment noise under each energy storage control feature, the specific method for obtaining the proportion of the noise response of the N noise components with the strongest noise response is as follows:

[0077] The N noise components with the strongest noise response are denoted as the first components; all the noise components other than the first components are denoted as the second components. The mean value of the noise responses of all the first components is denoted as x1, the mean value of the noise responses of all the second components is denoted as x2, and (x1 - x2) / (x1 + x2) is denoted as the proportion of the noise response. The proportion of the noise response obtained by this method focuses on describing the difference between the noise components with relatively strong noise responses and those with relatively weak noise responses.

[0078] In other examples, for the equipment noise under each energy storage control feature, the specific method for obtaining the proportion of the noise response of the N noise components with the strongest noise response is: taking the mean value of the proportions of the noise responses obtained in the above two examples to obtain the proportion of the noise response obtained in this example.

[0079] In this embodiment, N = 5 is taken as an example for description, and N anti-phase sound sources are placed around the microphone. In this embodiment, the placement position and quantity of the anti-phase sound sources are not limited. In some embodiments, the N anti-phase sound sources can be integrated into one device for easy carrying and placement.

[0080] As a comparative example, the method for noise suppression based on the N noise components with the strongest noise response is:

[0081] Changing the amplitude and phase of the N anti-phase sound sources in real time, and at each moment, making the amplitudes of these N anti-phase sound sources the same as the amplitudes and frequencies of the N noise components respectively, and the phases opposite.

[0082] Step S103: Denote the energy storage control feature with the largest difference A between the corresponding device noise and the current moment noise and the smallest difference B from the current moment energy storage control feature as the predicted control feature.

[0083] The above steps obtain the N noise components with the strongest noise response at each moment (or under each energy storage control feature). If directly using N anti-noise sources to suppress the N noise components with the strongest noise response at each moment (for example, when suppressing using the comparative example of step S102), the suppression efficiency is not high. The reason is that considering that the energy storage device operates with different energy storage control features under the regulation of the energy storage control system (specifically determined by the operating state of the power grid and the electricity consumption of grid users, which is a well-known technology), different device noises may be generated at different moments or time periods. When the energy storage control feature is moderately adjusted, part of the interference of the device noise can also be eliminated. However, when only using N anti-noise sources for noise suppression (for example, when suppressing using the comparative example of step S102), it will cause the energy storage control feature not to be adjusted to an appropriate value, which will not only affect the normal operation of the energy storage device but also prevent further suppression of the device noise based on the adjustment of the energy storage control feature of the energy storage device; or rather, when only using the comparative example of step S102 for suppression without considering the impact of the adjustment of the energy storage control feature of the energy storage device on the device noise, the suppression effect of the device noise is not optimal.

[0084] The predicted control feature described in this embodiment represents the energy storage control feature obtained after a certain degree of adjustment of the energy storage control feature; the interference of the device noise generated by the energy storage device operating under this predicted control feature will be significantly reduced; in the subsequent part of this embodiment, further noise suppression effect is achieved by combining N anti-noise sources based on this predicted control feature.

[0085] The current moment noise refers to the device noise collected at the current moment during the operation of the energy storage device. The current moment energy storage control feature refers to the energy storage control feature at the current moment during the operation of the energy storage device.

[0086] As a preferred example, the step of denoting the energy storage control feature with the largest difference A between the corresponding device noise and the current moment noise and the smallest difference B from the current moment energy storage control feature as the predicted control feature specifically includes:

[0087] For any energy storage control feature, the difference between the device noise corresponding to this energy storage control feature and the current moment noise is denoted as A; the difference between this energy storage control feature and the current moment energy storage control feature is denoted as B.

[0088] The difference between A and f×B is denoted as the reference degree of the energy storage control feature, and the energy storage control feature with the largest reference degree is used as the predictive control feature; f is denoted as the similarity suppression coefficient of the predictive control feature, and the initial value of f is a preset value.

[0089] Note that in some embodiments, in order to avoid the interference of the dimensions and orders of magnitude of A and B, it is necessary to remove the orders of magnitude and dimensions of A and B. One method is: perform linear normalization on the A values corresponding to all energy storage control features, and also perform linear normalization on the B values corresponding to all energy storage control features.

[0090] In other embodiments, other methods can be used to remove the orders of magnitude and dimensions of A and B, and this embodiment will not elaborate one by one.

[0091] For any energy storage control feature, the greater the reference degree of the energy storage control feature, it indicates that even if the energy storage control feature is changed by a small amplitude, the device noise at the current moment can be significantly changed (so as to weaken the device noise generated by the energy storage device). The smaller the reference degree, it indicates that even if the energy storage control feature is changed by a large amplitude, it is difficult to significantly change the device noise at the current moment (that is, it is difficult to weaken the device noise generated by the energy storage device).

[0092] As an example, the difference A between the corresponding device noise and the current moment noise is:

[0093] For any energy storage control feature, the average signal value of the device noise corresponding to the energy storage control feature is denoted as a1, and the average signal value of the current moment noise is denoted as a2. Denote a1 - a2 as the first difference, and let A be equal to this first difference.

[0094] In this example, A describes the difference in the signal intensity of the device noise as a whole. When the signal output by the microphone is severely affected by noise (for example, when the microphone has insufficient noise induction ability due to poor quality or abnormal operation), this example is preferably used for implementation.

[0095] As an example, the method for obtaining the average signal value includes:

[0096] For any device noise, the device noise is a time series sequence composed of the signals output by the microphone, and the mean value of the signal values in this time series sequence is denoted as the average signal value.

[0097] If the signal value in this time series sequence is less than 0, then the mean value of the absolute values of the signal values in this time series sequence is denoted as the average signal value.

[0098] As another example, the difference A between the corresponding device noise and the current moment noise is:

[0099] When a1 > a2, let k = 1, indicating that the overall signal strength of the device noise corresponding to this energy storage control feature is greater than the overall signal strength of the noise at the current moment; when a1 <= 1, k = -1, indicating that the overall signal strength of the device noise corresponding to this energy storage control feature is less than or equal to the overall signal strength of the noise at the current moment.

[0100] The DTW distance between the time series of the device noise corresponding to this energy storage control feature and the time series of the noise at the current moment is denoted as a, and let A = k × a.

[0101] Among them, the DTW distance is obtained by the DTW algorithm, which is a well-known technology. This algorithm can describe the differences in details between time series (such as differences in local change trends, etc.). Therefore, A obtained in this example can describe the differences in details between device noises (such as when there are local minor changes). When the signal output by the microphone is less affected by noise (for example, when the microphone has good quality and high precision), this embodiment is preferably used for implementation.

[0102] In some other embodiments, the difference A between the corresponding device noise and the noise at the current moment is: combining the above two examples, that is, A = (a1 - a2) × a.

[0103] As an example, the method for obtaining the difference (i.e., B) between the energy storage control feature and the energy storage control feature at the current moment includes:

[0104] The energy storage control feature includes charge and discharge rates, or SOC, DOD, etc. In this embodiment, the energy storage control feature is represented as a vector composed of these values. The difference B between the energy storage control feature and the energy storage control feature at the current moment is equal to the Euclidean distance between the vectors corresponding to the energy storage control feature and the energy storage control feature at the current moment.

[0105] It should be noted that in order to eliminate the interference of dimensions and orders of magnitude existing in the energy storage control feature, this embodiment uses ACA whitening processing. In other embodiments, other methods can be used to remove dimensions and orders of magnitude, and this embodiment will not be specifically described.

[0106] The above f is denoted as the similarity suppression coefficient of the predictive control feature. In some embodiments, f = 1 is used as an example for description. In other embodiments, f can also be set to other non-zero values.

[0107] Step S104: Select the predictive control feature with the largest noise suppression ratio from the predictive control features as the target control feature.

[0108] For the above similarity suppression coefficient, the larger the value is set, it indicates that the energy storage control characteristics obtained when adjusting the energy storage control characteristics of the energy storage device (i.e., the calculated predictive control characteristics) are less likely to cause the energy storage device to malfunction due to excessive adjustment of the energy storage control characteristics. For example, it cannot maintain the steady state of the large power grid or improve the power utilization rate of the power grid (such as weakening its peak shaving and valley filling ability). The smaller the value is, it indicates that the energy storage control characteristics obtained after adjusting the energy storage control characteristics of the energy storage device (i.e., the calculated predictive control characteristics) can significantly change the working conditions of the energy storage device. At this time, it is more possible to find the predictive control characteristics that can make the device noise suppression effect more obvious by adjusting the energy storage device.

[0109] The target control characteristics described in this embodiment represent the energy storage control characteristics that can suppress the device noise as much as possible (using N anti-phase sound sources) while minimizing the impact on the operation of the energy storage device.

[0110] As an example, the steps of selecting the predictive control characteristic with the largest noise suppression ratio from the predictive control characteristics as the target control characteristic include:

[0111] Preset the value range of the similarity suppression coefficient, obtain all the values of the similarity suppression coefficient within the value range. Under each value of the similarity suppression coefficient, a corresponding predictive control characteristic is obtained. Denote the predictive control characteristics with the largest noise suppression ratio and the largest similarity suppression coefficient as the target control characteristics.

[0112] As an example, the method for obtaining the value range of the similarity suppression coefficient is: set the value range of the similarity suppression coefficient as a fixed value range. For example, the value range of the similarity suppression coefficient is: 0.5, 0.6, 0.7, ……, 1.5.

[0113] As another example, the method for obtaining the value range of the similarity suppression coefficient is: set the value range of the similarity suppression coefficient as a dynamic value range, specifically including:

[0114] For all the vectors corresponding to the energy storage control characteristics (described in step S103), obtain the cosine similarity between any two vectors. Denote the mean value of all the obtained cosine similarities as m1. Select a number of values (such as 10 values) at equal intervals within the interval [1 - m1, 1 + m1] as the value range of the similarity suppression coefficient. In this method, the larger m1 is, it indicates that the variation range of the energy storage control characteristics is larger. At this time, the obtained value range allows the energy storage device to be adjusted with a relatively large adjustment amplitude; when m1 is smaller, it indicates that the variation range of the energy storage control characteristics is smaller. At this time, the obtained value range does not allow the energy storage device to be adjusted with a relatively large adjustment amplitude to avoid the energy storage device working under energy storage control characteristics that have never appeared.

[0115] As an example, the predictive control features with the largest noise suppression ratio and the largest similarity suppression coefficient are denoted as the target control features, and the method includes:

[0116] For any similarity suppression coefficient f1 within the value range, the predictive control feature obtained according to f1 is denoted as F1, the noise suppression ratio of F1 is denoted as q1, q1 + f1 is denoted as the evaluation index of f1, and the f1 with the largest evaluation index is denoted as fm. The predictive control feature obtained according to fm is denoted as the target control feature.

[0117] As another example, the predictive control features with the largest noise suppression ratio and the largest similarity suppression coefficient are denoted as the target control features, and the method includes:

[0118] y1×q1 + y2×f1 is denoted as the evaluation index of f1, where y1 and y2 are preset values. In this embodiment, y1 = 0.4 and y2 = 0.6. In other embodiments, y1 and y2 can be set to other values. In this embodiment, no specific limitations are made, as long as y1 and y2 are both greater than or equal to 0 and y1 + y2 = 1.

[0119] Specifically, in some embodiments, y2 can be set to 0, that is, the evaluation index of f1 is equal to q1. In these embodiments, any adjustment of the energy storage device can be realized, thereby suppressing noise to a greater extent, but there may be over-interference with the regulation of the energy storage device by the energy storage control system.

[0120] Step S105: Use an anti-sound source to suppress the device noise generated by the energy storage device operating under the target control feature.

[0121] The energy storage device operates with the target control feature at the next moment, and the device noise is collected, and the N noise components with the strongest noise response in the device noise are obtained. Then, N anti-sound sources are made to generate sound waves, and the phases of these sound waves are opposite to the phases of the N noise components with the strongest noise response in the device noise; the amplitudes and frequencies are the same as those of these N noise components.

[0122] In this embodiment, the working process of the energy storage device is appropriately regulated first, so that the device noise generated by the regulated energy storage device is minimized as much as possible, and at the same time, the reduced device noise can be suppressed as much as possible by the anti-sound source. In this embodiment, the noise is dynamically suppressed from both aspects of the control of the energy storage device and the control of the anti-sound source, improving the noise suppression effect.

[0123] Embodiment 2:

[0124] Please refer to Figure 2 , this embodiment provides a method for suppressing the noise of an energy storage device based on feedback. The method includes:

[0125] S201. Record the energy storage control characteristics and the corresponding device noise during the operation of the energy storage device. The energy storage control characteristics at least include the charge and discharge rate of the energy storage device.

[0126] This step is the same as step S101 in Embodiment 1.

[0127] S202. Among the device noises corresponding to each energy storage control characteristic, the noise response ratio of the N noise components with the strongest noise response is recorded as the noise suppression ratio of each energy storage control characteristic; N is the number of anti-sound sources.

[0128] This step is the same as step S102 in Embodiment 1.

[0129] Step S203. The energy storage control characteristic with the largest difference A between the corresponding device noise and the current moment noise and the smallest difference B between the energy storage control characteristic and the current moment energy storage control characteristic is recorded as the predicted control characteristic.

[0130] The specific method for obtaining the predicted control characteristic includes:

[0131] Obtain the difference A between the device noise corresponding to any energy storage control characteristic and the current moment noise, and obtain the difference B between any energy storage control characteristic and the current moment energy storage control characteristic.

[0132] Obtain the difference between A and B, and record this difference as the reference degree of the energy storage control characteristic. The energy storage control characteristic with the largest reference degree is used as the predicted control characteristic.

[0133] Step S204. Use the anti-sound source to suppress the device noise generated by the energy storage device operating under the predicted control characteristic.

[0134] In this embodiment, the predicted control characteristic represents the energy storage control characteristic obtained by fine-tuning the energy storage control characteristic. The interference of the device noise of the energy storage device operating under this predicted control characteristic is significantly reduced.

[0135] The energy storage device performs charge and discharge operations at the predicted control characteristic at the next moment, and the device noise is collected. This device noise is recorded as D0.

[0136] Let N anti-sound sources generate sound waves. The phases of these sound waves are opposite to the phases of the N noise components with the strongest noise response in the device noise D0; the amplitudes and frequencies are the same as those of these N noise components.

[0137] In the above process, by fine-tuning the working process of the energy storage device, the device noise generated by the regulated energy storage device is minimized as much as possible. On this basis, the anti-sound source is used for noise suppression, which has a high noise suppression efficiency.

[0138] Step S205: Re-obtain the prediction control feature using the maximum value of the noise suppression ratio of the energy storage control feature and the noise suppression ratio corresponding to D0, and perform noise suppression.

[0139] (1) For the maximum value of the noise suppression ratios of all energy storage control features, this maximum value is denoted as max.

[0140] Obtain the noise suppression ratio corresponding to D0, denoted as F0, and denote (max - F0) / max as the noise suppression error w of the prediction control feature. In other embodiments, it is also possible to set w = (max - F0) / M, where M is a preset value. For example, M = F0. In this embodiment, the value of M is not limited, as long as w is positively correlated with (max - F0).

[0141] In some other examples, the method for obtaining max further includes:

[0142] In a recent number of days (for example, within the most recent week), the maximum value of the noise suppression ratios of all energy storage control features is denoted as max.

[0143] As a preferred example, the method for obtaining max further includes:

[0144] Perform mean shift clustering on the noise suppression ratios of all energy storage control features to obtain all categories, calculate the mean value of all noise suppression ratios within each category, and obtain the category with the smallest difference (that is, the smallest absolute value of the difference) between the mean value and F0. The maximum value of the noise suppression ratio in this category is denoted as max.

[0145] Using the max obtained in the preferred example to obtain the noise suppression error w can, to a certain extent, solve the following problems:

[0146] For all energy storage control features collected during the historical operation of the energy storage device, the energy storage control feature with the largest noise suppression ratio may not be applicable to the current working condition of the energy storage device. For example, the working time period of the energy storage device (or seasonal weather conditions affecting the power grid power consumption, etc.) is different, resulting in the energy storage control feature with the largest noise suppression ratio not being applicable to the current working condition of the energy storage device. At this time, directly taking the maximum value of the noise suppression ratios of all energy storage control features as max may cause an error in the calculation result of the noise suppression error w. For example, sudden changes in weather or sudden changes in the steady state of the power grid (or power grid failures) within a certain period of time cause abnormal changes in the energy storage control features of the energy storage device, and such abnormal changes lead to an error in the calculation result of the noise suppression error w.

[0147] For the first two examples of obtaining max, when implemented under the condition of continuous and stable operation of the power grid, accurate and fast effects can be achieved.

[0148] For the noise suppression error of obtaining the predictive control feature as described above, the larger the noise suppression error, it indicates that although the predictive control feature obtained in step S203 can make the device noise generated by the regulated energy storage device as small as possible through fine-tuning, this predictive control feature cannot optimize the noise suppression effect, that is, it does not achieve the noise suppression effect when the noise suppression ratio is the largest (or the noise suppression effect can be further optimized).

[0149] (2) Denote f = 1 - w as the similarity suppression coefficient of the predictive control feature.

[0150] In some other embodiments, f = 1 - g×w can be set, where g is a preset value, for example, g = 0.8. In other embodiments, g can also be set to other values, as long as f is greater than 0 and is negatively correlated with w.

[0151] The similarity suppression coefficient of other energy storage control features other than the predictive control feature is defaulted to 1. This process is equivalent to an update of the similarity suppression coefficient of the predictive control feature.

[0152] For the current moment of the working process of the energy storage device (that is, the current moment after the next moment described in step S204), re-obtain the difference A between the device noise corresponding to any energy storage control feature and the current moment noise, and the difference B between any energy storage control feature and the current moment energy storage control feature.

[0153] Obtain the difference between A and f×B, and denote this difference as the reference degree of the energy storage control feature. Take the energy storage control feature with the largest reference degree as the predictive control feature.

[0154] (3) According to the method of step S204, suppress the device noise generated by the energy storage device working under the predictive control feature by using an anti-sound source.

[0155] (4) Then, use the method in S205 again to obtain the noise suppression error w of the predictive control feature. And update the similarity suppression coefficient of the predictive control feature. Further, obtain the predictive control feature again. After that, according to the method of step S204, suppress the device noise generated by the energy storage device working under the predictive control feature by using an anti-sound source.

[0156] By repeatedly executing the methods in (1), (2), (3), and (4) above in S205, and feeding back the noise suppression error during the previous noise suppression to the next noise suppression process, the effect of real-time updating of the similarity suppression coefficient (or predictive control feature) of the predictive control feature according to the noise suppression error of the predictive control feature is achieved. That is, when the noise suppression error is larger (the similarity suppression coefficient is smaller), at this time, the energy storage device can be adjusted more to find the predictive control feature that makes the noise suppression effect of the device more obvious; when the noise suppression error is smaller (that is, the similarity suppression coefficient is larger), in addition to being able to further suppress noise after adjusting the energy storage control feature of the energy storage device (that is, the calculated predictive control feature), the energy storage device will not be unable to work properly due to excessive adjustment of the energy storage control feature. As the energy storage device continues to work and the similarity suppression coefficients of all energy storage control features are updated, it is possible to efficiently suppress noise during noise suppression and minimize the impact on the working condition of the energy storage device.

[0157] In summary, both Embodiment 1 and Embodiment 2 of the present invention use the automatic control technology of the anti-phase sound source and the automatic control technology of the energy storage device to suppress noise, and these two technologies are reasonably combined. Among them, the automatic control technology of the energy storage device can initially reduce the generation of noise, and the automatic control technology of the anti-phase sound source can further suppress noise on this basis.

[0158] In addition, according to step S104 of Embodiment 1 and step S205 of Embodiment 2, it can be seen that compared with the separate use of the automatic control technology of the anti-phase sound source or the automatic control technology of the energy storage device, the noise suppression efficiency and effect of all embodiments of the present invention are better.

[0159] Embodiment 3:

[0160] This embodiment provides another noise suppression method, which is a combination of the methods described in Embodiment 1 and Embodiment 2 (this embodiment provides two combination methods).

[0161] First of all, it should be noted that the noise suppression method described in Embodiment 1 obtains the target control feature through different values of the similarity suppression coefficient, and then suppresses noise. One of the advantages of this method is that it can limit the adjustment range of the energy storage device by specifying the value range of the similarity suppression coefficient, and can also suppress noise to a large extent each time; the disadvantage is that when the value range of the similarity suppression coefficient is not accurately determined, it will affect the final effect of noise suppression and the response speed is slow.

[0162] In contrast, the noise suppression method described in the second embodiment continuously updates the similarity suppression coefficient, and then selects appropriate predictive control features for noise suppression. The advantage of this method is that it can determine the value of the similarity suppression coefficient through error feedback, and determine the next noise suppression method based on the noise suppression situation at the previous moment. This method has a fast response speed (i.e., less computational effort) during long-term operation. The disadvantage is that it cannot limit the adjustment range of the energy storage device. In addition, although it can suppress noise to a large extent on a long time scale, the noise suppression effect may not be optimal at specific local times (e.g., within several local moments).

[0163] (1) The first combination method:

[0164] During peak electricity consumption periods, the power grid is prone to imbalance, and energy storage devices (such as the peak shaving and valley filling function of energy storage devices) are needed to maintain the stability of the power grid. At this time, the method in the first embodiment is used for noise suppression. The reason is that this process avoids excessive adjustment of the energy storage device and affects the steady-state operation of the power grid by limiting the value range of the similarity suppression coefficient.

[0165] During low electricity consumption periods, the power grid is not prone to imbalance. At this time, the method in the second embodiment is used for noise suppression, with a faster response speed (i.e., when each 0.5 seconds or each 0.1 seconds is a moment, noise suppression can also be performed at each moment), and the noise suppression effect is better.

[0166] The method for dividing peak and low electricity consumption periods is well-known. For example, if the average daytime temperature is greater than 28 degrees Celsius and the average nighttime temperature is less than 15 degrees Celsius, then that day is considered a peak electricity consumption period, otherwise it is a low electricity consumption period. Other division methods can be used in other embodiments, and this embodiment does not specifically limit them.

[0167] (2) The second combination method:

[0168] Obtain the target control feature according to the method in step S104 of the first embodiment (the similarity suppression coefficient of all energy storage control features is set to 1) in this process.

[0169] The energy storage device performs charge and discharge operations with the target control feature at the next moment, and the device noise collected is denoted as D0. And according to the method in step S105 of the first embodiment, suppress the device noise generated by the energy storage device operating under the target control feature.

[0170] It should be noted that the target control feature at this time is equivalent to the predictive control feature described in step S205 of the second embodiment.

[0171] Then, the noise suppression error w of the target control feature is obtained by using the method in step S205 of Embodiment 2, and the similarity suppression coefficient of the target control feature is updated to f = 1 - w.

[0172] When performing the next (or at the next moment) noise suppression, the target control feature is obtained again according to the method in step S104 of Embodiment 1. Note that at this time, the value range of the similarity suppression coefficient in step S104 of Embodiment 1 is set to r×f, where r is any value in the preset value range, and this preset value range is the same as the value range set in step S104 of Embodiment 1. For example, a value range composed of 0.5, 0.6, 0.7,..., 1.5 (a discrete value range) is used.

[0173] Then, according to the method in step S105 of Embodiment 1, the equipment noise generated by the energy storage device operating under the target control feature is suppressed.

[0174] Then, the noise suppression error w of the target control feature is obtained by using the method in step S205 of Embodiment 2, and the similarity suppression coefficient of the target control feature is updated to f = 1 - w.

[0175] And so on, by continuously updating the value range of the similarity suppression coefficient, the noise suppression effect is further improved.

[0176] Compared with Embodiment 1, the second combination method reasonably and dynamically updates the value range of the similarity suppression coefficient, further improving the noise suppression effect.

[0177] Embodiment 4:

[0178] Based on the second combination method described in Embodiment 3, a method for obtaining a target control feature is provided.

[0179] In the second combination method described in Embodiment 3, the method for obtaining the target control feature includes:

[0180] The value range of the similarity suppression coefficient can be obtained by using the method described in the second combination method in Embodiment 3, or by using the method in Embodiment 1. This embodiment does not make a limitation.

[0181] For any similarity suppression coefficient f1 within the value range, the predicted control feature obtained according to f1 is denoted as F1, the noise suppression ratio of F1 is denoted as q1, y1×q1 + y2×f1 is denoted as the evaluation index of f1, and the f1 with the largest evaluation index is denoted as fm. The predicted control feature obtained according to fm is denoted as the target control feature.

[0182] Among them, y1 is positively correlated with the noise suppression error w of the target control feature obtained in the second combination method in Embodiment 3. For example, y1 = kk × w, and y2 = 1 - y1. Additionally, when y2 is less than 0, let y1 = 1 and y2 = 0. In this embodiment, kk = 0.5 is taken as an example for description. In other embodiments, kk can be set to other values, which is not specifically limited in this embodiment.

[0183] In this embodiment, when w is larger, y1 is larger. At this time, it indicates that when adjusting the energy storage device based on the target control feature, although the device noise generated by the adjusted energy storage device can be minimized through adjustment, the predictive control feature cannot optimize the noise suppression effect, that is, it does not achieve the noise suppression effect when the noise suppression ratio is the largest. At this time, the evaluation index of f1 pays more attention to the noise suppression ratio rather than the magnitude of the similarity suppression system f1. Ultimately, it is achieved that when finely adjusting the energy storage device within a given value range (or when ensuring no excessive adjustment), if the optimal noise suppression effect cannot be achieved, then a slightly larger adjustment of the energy storage device is allowed (that is, weakening the constraint of the condition of no excessive adjustment), so as to make the noise suppression effect better.

[0184] When w is smaller, y1 is smaller. At this time, it indicates that when adjusting the energy storage device based on the target control feature, the noise suppression effect has approached the optimal value. At this time, the magnitude of the similarity suppression system f1 is focused on, and ultimately it is achieved that while the noise suppression effect approaches the optimal value, no excessive adjustment is made to the energy storage device.

[0185] In summary, this embodiment realizes that during the noise suppression process, it dynamically determines whether to allow the energy storage device to be excessively adjusted based on the noise suppression effect, further improving the noise suppression effect of the entire noise suppression process. This embodiment is applicable to scenarios where the impact of the working changes of the energy storage device on the power grid is small during the long-term stable operation of the power grid.

[0186] It should be noted that different embodiments of the present invention will have different noise suppression effects when applied to different implementation scenarios. However, compared with the existing methods of solely using anti-noise sources or adding sound insulation enclosures, all embodiments can improve the noise suppression effect.

[0187] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A noise sensing and suppression method for energy storage equipment, characterized in that: The method comprises the following steps: Recording energy storage control characteristics and corresponding device noise when the energy storage device is working, wherein the energy storage control characteristics at least include a charge and discharge rate of the energy storage device; In the device noise corresponding to each energy storage control feature, the noise response ratio of the N noise components with the strongest noise response is recorded as the noise suppression ratio of each energy storage control feature; N is the number of anti-phase sound sources; the anti-phase sound source refers to a device that can generate a specified sound wave; For the difference A between the device noise corresponding to any energy storage control feature and the noise at the current moment, and the difference B between any energy storage control feature and the energy storage control feature at the current moment, the energy storage control feature when the difference between A and f×B is the largest is used as the prediction control feature; f is recorded as the similarity suppression coefficient corresponding to the prediction control feature, and the initial value of f is a preset value; Obtain the noise suppression ratio of the predictive control feature obtained under different values ​​of the similarity suppression coefficient, and record the predictive control feature with the largest noise suppression ratio and similarity suppression coefficient as the target control feature; The energy storage device operates with the target control characteristics at the next moment and records the device noise D0 collected in real time. The sound waves generated by the anti-phase sound source have the same amplitude and frequency and opposite phases as the N noise components with the strongest noise response in D0.

2. A noise sensing and suppression method for energy storage equipment according to claim 1, characterized in that: The step of obtaining the noise suppression ratio of the predictive control feature obtained under different values ​​of the similarity suppression coefficient and recording the predictive control feature with the largest noise suppression ratio and similarity suppression coefficient as the target control feature includes the following specific steps: A value range of the similarity suppression coefficient is preset. For any similarity suppression coefficient f1 within the value range, the prediction control feature corresponding to f1 is recorded as F1, the noise suppression ratio of F1 is recorded as q1, q1+f1 is recorded as the evaluation index of f1, f1 with the largest evaluation index is recorded as fm, and the prediction control feature corresponding to fm is recorded as the target control feature.

3. A feedback-based energy storage device noise suppression method, characterized in that: The method includes: Recording energy storage control characteristics and corresponding device noise when the energy storage device is working, wherein the energy storage control characteristics at least include a charge and discharge rate of the energy storage device; In the device noise corresponding to each energy storage control feature, the noise response ratio of the N noise components with the strongest noise response is recorded as the noise suppression ratio of each energy storage control feature; N is the number of anti-phase sound sources; the anti-phase sound source refers to a device that can generate a specified sound wave; For the difference A between the device noise corresponding to any energy storage control feature and the noise at the current moment, and the difference B between any energy storage control feature and the energy storage control feature at the current moment, the energy storage control feature when the difference between A and B is the largest is used as the prediction control feature; S1: The energy storage device operates with predictive control characteristics at the next moment and collects device noise D0; the sound waves generated by the anti-phase sound source have the same amplitude and frequency, but opposite phases as the N noise components with the strongest noise response in D0; S2: The difference between the maximum value max of the noise suppression ratio of the energy storage control feature and the noise suppression ratio corresponding to D0 obtains the noise suppression error w of the predictive control feature, and obtains the similarity suppression coefficient f of the predictive control feature, and f is negatively correlated with w; S3: obtaining the prediction control feature again, wherein the energy storage control feature when the difference between A and f×B is the largest is used as the prediction control feature; S4: suppressing the device noise of the energy storage device by repeatedly executing S1, S2 and S3.

4. The method for suppressing noise of energy storage equipment based on feedback according to claim 3, characterized in that: The specific steps for obtaining the N noise components with the strongest noise response in the device noise corresponding to each energy storage control feature are as follows: The device noise under each energy storage control feature is decomposed into several noise components by using Fourier transform algorithm, the amplitude of the noise component is used as the noise response of the noise component, and the N noise components with the largest noise response are used as the N noise components with the strongest noise response.

5. The method for suppressing noise of energy storage equipment based on feedback according to claim 3, characterized in that: The specific steps for obtaining the noise response ratio are as follows: The N noise components with the strongest noise responses are recorded as first components; the ratio of the mean value of the noise response of the first components to the mean value of the noise response of all noise components of the device noise is recorded as the noise response ratio.

6. The method for suppressing noise of energy storage equipment based on feedback according to claim 3, characterized in that: The specific steps for obtaining the difference A between the device noise corresponding to the arbitrary energy storage control feature and the noise at the current moment are as follows: The current moment noise is the device noise collected at the current moment during the operation of the energy storage device, and the device noise is expressed as a time series; obtaining a first difference between the device noise and the average signal value of the time series corresponding to the current moment noise, and obtaining a DTW distance between the device noise and the time series corresponding to the current moment noise; The A is positively correlated with the first difference and the DTW distance respectively.

7. The method for suppressing noise of energy storage equipment based on feedback according to claim 3, characterized in that: The specific steps for obtaining the difference B between the arbitrary energy storage control characteristic and the energy storage control characteristic at the current moment are as follows: The energy storage control feature at the current moment represents the energy storage control feature at the current moment during the operation of the energy storage device, and the energy storage control feature is represented as a vector; The Euclidean distance between the energy storage control feature and the vector corresponding to the energy storage control feature at the current moment is taken as the value of B.

8. The method for suppressing noise of energy storage equipment based on feedback according to claim 3, characterized in that: The specific steps for obtaining the maximum value max of the noise suppression ratio of the energy storage control feature are as follows: The noise suppression ratios of all energy storage control features are clustered by mean shift to obtain all categories. The mean of all noise suppression ratios in each category is calculated to obtain the category with the smallest difference between the mean of all noise suppression ratios and F0. The maximum value of the noise suppression ratio in this category is recorded as max; F0 represents the noise suppression ratio corresponding to D0.

9. A feedback-based energy storage device noise suppression method according to claim 8, characterized in that: The noise suppression error w=(max-F0) / max.

10. The method for suppressing noise of energy storage equipment based on feedback according to claim 3, characterized in that: The similarity suppression coefficient f=1-w.

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