Surge protection method, device, system, computer device, medium and battery

Abstract

The application relates to a surge protection method, device, system, computer equipment, medium and battery. Output state data of a charging device can be acquired to input a preset surge risk model for machine learning analysis to determine a predicted surge risk level of a surge risk occurring under a current state. After that, the number of surge suppressors between the battery management unit and the charging device is dynamically adjusted in combination with the predicted surge risk level to suppress the output surge of the charging device. Through the scheme, the output surge of the charging device can be predicted in combination with the output state data, and the surge suppression capacity can be dynamically adjusted according to the prediction result, the surge risk can be early warned, and the surge suppressors are connected in operation in a suitable number. In this way, the prediction and protection of the abnormal surge output by the charging device can be realized, the risk of the battery management unit suffering from electrical overstress is greatly reduced, and the occurrence of the battery management unit ablation is alleviated.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of charge protection, in particular to a surge protection method, device, system, computer equipment, medium and battery. BACKGROUND

[0002] With the rapid development of new energy technology, the coverage rate of new energy power vehicles is gradually improved, and the demand for charging piles is gradually increasing.

[0003] In the related art, during the charging process of the charging pile on the new energy vehicle, abnormal surges are easily output, which causes the battery management unit to suffer from electrical overstress, and further causes internal ablation problems. SUMMARY

[0004] Therefore, it is necessary to provide a surge protection method, device, system, computer equipment, medium and battery to protect the abnormal surges output by the charging equipment, reduce the risk of the battery management unit suffering from electrical overstress, and alleviate the occurrence of the ablation of the battery management unit.

[0005] The application provides a surge protection method, which comprises the following steps: acquiring output state data of a charging equipment; performing machine learning analysis on the output state data and a preset surge risk model to determine a predicted surge risk level; and dynamically adjusting the number of surge suppressors connected between a battery management unit and the charging equipment according to the predicted surge risk level.

[0006] The above scheme is characterized in that a plurality of surge suppressors are arranged in front of the battery management unit of the battery to be charged. During the charging process of the charging equipment on the battery to be charged, the output state data of the charging equipment can be acquired to input the preset surge risk model to perform machine learning analysis and determine the predicted surge risk level of the surge risk under the current state. Then, the number of surge suppressors connected between the battery management unit and the charging equipment is dynamically adjusted according to the predicted surge risk level to suppress the output surges of the charging equipment. Through the scheme, the output surges of the charging equipment can be predicted according to the output state data, and the surge suppression capacity can be dynamically adjusted according to the prediction result. The surge risk can be warned in advance, and the surge suppressors are connected in operation in an appropriate number. In this way, the prediction and protection of the abnormal surges output by the charging equipment can be realized, the risk of the battery management unit suffering from electrical overstress can be greatly reduced, and the occurrence of the ablation of the battery management unit can be alleviated.

[0007] In some embodiments, the machine learning analysis according to the output state data and the preset surge risk model determines a predicted surge risk level, including: performing feature extraction analysis according to the output state data to determine a feature vector parameter; and performing machine learning analysis according to the feature vector parameter and the preset surge risk model to determine the predicted surge risk level.

[0008] The above scheme, after obtaining the output state data, needs to perform feature extraction on the output state data, and input the feature vector parameter into the preset surge risk model to perform machine learning analysis. In this way, the data type input into the machine learning analysis can be enriched, and the accuracy of the machine learning result can be improved.

[0009] In some embodiments, the feature extraction analysis according to the output state data determines a feature vector parameter, including: performing feature extraction analysis according to the output state data to determine an initial feature vector parameter; and performing dimension reduction analysis according to the initial feature vector parameter to determine the feature vector parameter.

[0010] The above scheme first performs feature extraction analysis to determine an initial feature vector parameter, and then performs dimension reduction analysis on the initial feature vector parameter to determine the feature vector parameter. In this way, the data storage space can be reduced, the complexity of subsequent calculation can be reduced, and the execution efficiency of surge protection can be improved.

[0011] In some embodiments, the output state data includes an output current parameter, an output voltage parameter, and a noise parameter; the feature extraction analysis according to the output state data determines an initial feature vector parameter, including: performing at least one of time domain analysis, frequency domain analysis, and wavelet transform analysis on the output current parameter and the output voltage parameter to determine an extracted feature parameter; and determining the initial feature vector parameter according to the extracted feature parameter and the noise parameter.

[0012] The above scheme combines the output current parameter and the output voltage parameter to perform at least one of time domain analysis, frequency domain analysis, and wavelet transform analysis, and then combines the noise parameter to determine the initial feature vector parameter, which can effectively improve the richness of the initial feature vector parameter.

[0013] In some embodiments, the output state data further includes a temperature compensation parameter and / or an aging compensation parameter, and the method further includes: determining the initial feature vector parameter according to the extracted feature parameter, the temperature compensation parameter, and / or the aging compensation parameter.

[0014] The above scheme further considers the temperature compensation parameter and / or the aging compensation parameter on the basis of the output current parameter, the output voltage parameter, and the noise parameter, improves the data dimension input into the preset surge risk model, and further improves the accuracy of the prediction result.

[0015] In some embodiments, the extracted characteristic parameters include at least one of a voltage peak value, a current peak value, a voltage rise time, a current rise time, a voltage pulse density, a current pulse density, a voltage waveform kurtosis, a current waveform kurtosis, a voltage total harmonic distortion, a current total harmonic distortion, a voltage spectral centroid, a current spectral centroid, a voltage high-frequency energy ratio, a current high-frequency energy ratio, a voltage wavelet energy, a current wavelet energy, a voltage wavelet entropy, and a current wavelet entropy.

[0016] The above scheme, the extracted characteristic parameters can be selected according to actual scene or demand, and at least one of time domain analysis, frequency domain analysis and wavelet conversion analysis is obtained, which effectively improves the adaptability of the surge protection method.

[0017] In some embodiments, the higher the predicted surge risk level, the more surge suppressors are connected between the battery management unit and the charging device.

[0018] The above scheme, in the case of a high predicted surge risk level, more surge suppressors are connected to suppress surges, so that the surge suppression capability is adapted to the predicted surge risk level, and the surge suppression accuracy is improved.

[0019] In some embodiments, the number of surge suppressors connected between the battery management unit and the charging device is dynamically adjusted according to the predicted surge risk level, including: in the case of the predicted surge risk level being a first level, a first number of the surge suppressors are controlled to be connected between the battery management unit and the charging device; in the case of the predicted surge risk level being a second level, a second number of the surge suppressors are controlled to be connected between the battery management unit and the charging device; wherein the risk degree of the second level is higher than that of the first level, and the second number is greater than the first number; in the case of the predicted surge risk level being a third level, a third number of the surge suppressors are controlled to be connected between the battery management unit and the charging device; wherein the risk degree of the third level is higher than that of the second level, and the third number is greater than the second number.

[0020] The above scheme divides the predicted surge risk level into three different levels, and connects different numbers of surge suppressors under different levels. In this way, different surge protection strategies can be distinguished with fewer risk levels, and the surge protection operation reliability is improved.

[0021] In some embodiments, the method further comprises: in the case that the predicted surge risk level reaches a target risk level, switching a backup branch access for output state data acquisition; in the case that the predicted surge risk level does not reach the target risk level, maintaining a main branch access for output state data acquisition; wherein the high-frequency noise suppression capability of the backup branch is stronger than that of the main branch.

[0022] The above scheme can perform output state data acquisition through different branch accesses, and in the case that the predicted surge risk level reaches a target risk level, the backup branch access with stronger high-frequency noise suppression capability is switched to run. In this way, the high-frequency noise suppression capability is improved, and the backup branch can keep the main branch running when the backup branch is damaged at the target risk level.

[0023] In some embodiments, the method further comprises: in the case that the predicted surge risk level reaches a target risk level, outputting a reduced-power charging instruction; in the case that the predicted surge risk level does not reach the target risk level, maintaining the current charging power charging operation.

[0024] The above scheme can output a reduced-power charging instruction in the case that the predicted surge risk level reaches a target risk level, thereby reducing the output power of the charging device and improving charging safety.

[0025] In some embodiments, the method further comprises: obtaining health state monitoring information of the surge suppressor; and dynamically adjusting the number of surge suppressors accessed between the battery management unit and the charging device according to the predicted surge risk level and the health state monitoring information.

[0026] The above scheme further monitors the health state of the surge suppressor, and combines the health state monitoring information and the predicted surge risk level to access the surge suppressor in good health state for operation, thereby improving the surge suppression reliability.

[0027] In some embodiments, the method further comprises: in the case that a forced target risk level execution condition is met, adjusting the number of surge suppressors accessed between the battery management unit and the charging device corresponding to the target risk level; and in the case that the forced target risk level execution condition is not met, performing the step of dynamically adjusting the number of surge suppressors accessed between the battery management unit and the charging device according to the predicted surge risk level.

[0028] The above scheme, in the case that the forced target risk level execution condition is met, no longer makes a decision according to the predicted surge risk level, but directly accesses the surge suppressors corresponding to the target risk level for operation, thereby further improving the surge protection reliability.

[0029] In some embodiments, the condition of the forced target risk level execution is determined to be met when at least one of the following conditions is met: a first condition that a current pulse density determined according to the output state data is greater than a preset current density threshold; a second condition that a voltage pulse density determined according to the output state data is greater than a preset voltage density threshold; a third condition that a current change rate determined according to the output state data is greater than a preset current change threshold; and a fourth condition that a voltage change rate determined according to the output state data is greater than a preset voltage change threshold.

[0030] The above scheme can combine at least one of the current pulse density, the voltage pulse density, the current change rate, and the voltage change rate to determine whether the condition of the forced target risk level execution is met, and has high accuracy in identifying the condition of the forced target risk level execution.

[0031] In some embodiments, the method further comprises updating the preset surge risk model according to the output state data obtained in real time.

[0032] The above scheme can also update the output state data and the preset surge risk model, thereby greatly improving the accuracy of the preset surge risk model.

[0033] The present application also provides a surge protection device, comprising: a data acquisition module configured to acquire output state data of a charging device; a risk prediction module configured to perform machine learning analysis according to the output state data and a preset surge risk model to determine a predicted surge risk level; and a dynamic protection module configured to dynamically adjust the number of surge suppressors connected between a battery management unit and the charging device according to the predicted surge risk level.

[0034] The present application also provides a surge protection system, comprising a surge suppression array circuit, a monitoring assembly, and a controller. The monitoring assembly is arranged between the surge suppression array circuit and a battery management unit. The surge suppression array circuit comprises a normally closed surge assembly and at least one controllable surge branch. The controllable surge branch is connected in parallel with the normally closed surge assembly, and a first end formed by the parallel connection is connected to a charging device, and a second end formed by the parallel connection is connected to the battery management unit. The controllable surge branch comprises a controllable switch and a surge suppressor connected in series. The monitoring assembly and the controllable switch are connected to the controller, respectively. The controller is configured to perform the steps of the above surge protection method.

[0035] In some embodiments, the normally closed surge assembly comprises a surge suppressor and a self-resetting fuse connected in series.

[0036] The above scheme further comprises a self-resetting fuse connected in series with the surge suppressor in the normally closed surge assembly. The self-resetting fuse can limit the current when the current is too large, thereby improving the operation reliability of the surge suppression array circuit.

[0037] In some embodiments, the system further comprises a pressure-sensitive resistor and / or a common-mode choke coil arranged in series between the surge suppression array circuit and the charging device.

[0038] The above scheme can realize the absorption and / or suppression of high-energy surges and common-mode interference by arranging a pressure-sensitive resistor and / or a common-mode choke coil in series between the surge suppression array circuit and the charging device, thereby further improving the operation reliability of the surge protection system.

[0039] In some embodiments, the charging device is connected to the surge suppression array circuit through a positive charging line and a negative charging line, and a gas discharge tube is further arranged in parallel between the positive charging line and the negative charging line.

[0040] The above scheme can further discharge ultra-high-energy surges by arranging a gas discharge tube in parallel with the charging line connecting the charging device and the surge suppression array circuit, thereby further improving the operation reliability of the surge protection system in a multi-level protection manner.

[0041] In some embodiments, the system further comprises a switching switch, a main branch, and a backup branch, an input end of the switching switch is connected to the surge suppression array circuit, a first output end of the switching switch is connected to the monitoring component through the main branch, a second output end of the switching switch is connected to the monitoring component through the backup branch, the switching switch is connected to the controller, and the backup branch has a higher high-frequency noise suppression capability than the main branch.

[0042] The above scheme can realize output state data acquisition through different branches, and in the case that the predicted surge risk level reaches a target risk level, the backup branch with a higher high-frequency noise suppression capability is switched to run. In this way, the high-frequency noise suppression capability can be improved, and the system has a certain redundancy capability, and the main branch can be kept running when the backup branch is damaged at the target risk level.

[0043] The application further provides a battery comprising a battery management unit, a battery cell, and the above surge protection system.

[0044] The application further provides a computer device comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above surge protection method when executing the computer program.

[0045] The application further provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the steps of the above surge protection method when executed by a processor. BRIEF DESCRIPTION OF DRAWINGS

[0046] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments with reference made to the accompanying drawings. The drawings are for purposes of illustration only and are not intended to be limiting in any respect. Furthermore, like reference numerals are used to designate throughout the Figures the same elements. In the drawings:

[0047] Figure 1 Flowchart of a surge protection method according to some embodiments of the present application;

[0048] Figure 2 Block diagram of a surge protection system according to some embodiments of the present application;

[0049] Figure 3 Flowchart of a surge protection method according to some other embodiments of the present application;

[0050] Figure 4 Flowchart of a surge risk level prediction according to some embodiments of the present application;

[0051] Figure 5 Flowchart of a data processing according to some embodiments of the present application;

[0052] Figure 6 Flowchart of a data processing according to some other embodiments of the present application;

[0053] Figure 7 Flowchart of a surge protection method according to some further embodiments of the present application;

[0054] Figure 8 Flowchart of a surge protection method according to some more embodiments of the present application;

[0055] Figure 9 Flowchart of a surge protection method according to some other embodiments of the present application;

[0056] Figure 10 Flowchart of a surge protection method according to some further embodiments of the present application;

[0057] Figure 11 Flowchart of a surge protection method according to some more embodiments of the present application;

[0058] Figure 12 Block diagram of a surge protection device according to some embodiments of the present application;

[0059] Figure 13 Block diagram of a surge protection device according to some other embodiments of the present application;

[0060] Figure 14 Block diagram of a surge protection device according to some further embodiments of the present application;

[0061] Figure 15 Structure diagram of surge protection device in some embodiments of the present application;

[0062] Figure 16 Structure diagram of surge protection system in some embodiments of the present application;

[0063] Figure 17 Structure diagram of surge protection system in some embodiments of the present application;

[0064] Figure 18 Structure diagram of surge protection system in some embodiments of the present application. DETAILED DESCRIPTION

[0065] The embodiments of the technical solutions of the present application will be described in detail below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and therefore only serve as examples, and cannot limit the protection scope of the present application.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "include" and "have" and any variations thereof in the specification and claims of the present application and the above description of drawings are intended to cover non-exclusive inclusion.

[0067] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0068] In this paper, the phrase "embodiment" means that the specific features, structures or properties described in conjunction with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor is it an independent or alternative embodiment that is not mutually exclusive with other embodiments. The skilled person in the art explicitly and implicitly understands that the embodiments described herein can be combined with other embodiments.

[0069] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents a "or" relationship between the associated objects before and after it.

[0070] In the description of the embodiments of the present application, the term "a plurality of" refers to two or more (including two), and similarly, "a plurality of groups" refers to two or more groups (including two groups), and "a plurality of pieces" refers to two or more pieces (including two pieces).

[0071] In the description of the embodiments of the present application, unless otherwise explicitly specified and limited, the technical terms "mounting", "connecting", "connecting", "fixing" and the like should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0072] At present, from the development of market situation, the application of battery is more and more extensive. Not only is it applied to energy storage power supply systems such as hydroelectric, thermal, wind and solar power stations, but also is widely used in electric bicycles, electric motorcycles, electric vehicles and other electric vehicles, as well as aerospace and other fields. With the continuous expansion of the application field of battery, the demand of its market is also increasing.

[0073] These battery-using devices often need to charge the battery through a charging device when the battery power is depleted or about to be depleted. However, in the actual charging process, the battery management unit of the battery is prone to electrical overstress, which in turn causes internal burnout. Through research, it is found that abnormal surges are the root cause of this phenomenon, whether it is an abnormal surge caused by power grid fluctuations, or due to the fact that the charging device is in an outdoor environment for a long time, the insulation performance decreases (such as humidity, salt spray, aging, etc.), resulting in insulation failure between the low-voltage loop and the high-voltage part. After insulation breakdown, high-voltage electricity directly enters the low-voltage loop, causing a surge impact, which will cause the above phenomenon.

[0074] To alleviate the above phenomenon, a surge suppressor capable of suppressing surges can be considered to be configured between the front end of the battery management unit, i.e. between the battery management unit and the charging device (such as a charging pile), to suppress surges. Further, considering that the required surge suppression capability is different under different surge conditions, the output state data of the battery management unit of the charging device output to the battery to be charged can be monitored to predict the risk level of the surge, and according to the prediction result, different number of surge suppressors are connected, so that the actual surge suppression capability is adapted to the predicted surge risk level, prolonging the service life of the surge suppressor and optimizing the system life of the surge protection system.

[0075] Based on the above considerations, the application provides a surge protection method, a plurality of surge suppressors are arranged at the front end of the battery management unit of the battery to be charged, and the output state data of the charging device can be obtained during the charging of the charging device to the battery to be charged. The output state data is input into a preset surge risk model for machine learning analysis to determine the predicted surge risk level of the surge risk under the current state. After that, the number of surge suppressors connected between the battery management unit and the charging device is dynamically adjusted in combination with the predicted surge risk level to suppress the output surge of the charging device.

[0076] Through the above scheme, the output surge of the charging device can be predicted in combination with the output state data, and the surge suppression capacity can be dynamically adjusted according to the prediction result, so that the surge risk can be warned in advance, and the surge suppressors are connected in operation in an appropriate number. In this way, the prediction and protection of abnormal surges output by the charging device can be realized, the risk of the battery management unit suffering from electrical overstress can be greatly reduced, and the occurrence of the battery management unit ablation can be alleviated.

[0077] The surge protection method provided by the embodiments of the application is used for surge prediction and protection between the charging device and the battery management unit of the battery to be charged. The type of the charging device is not unique, which can be a direct current charging device or an alternating current charging device, and is not limited in particular. The battery to be charged can be configured in any electrical device, including but not limited to electric vehicles, electric motorcycles, electric bicycles, etc., and is also not limited in particular. In order to facilitate understanding, the following embodiments assume that the charging device is a charging pile and the battery to be charged is a power battery of an electric vehicle.

[0078] Please refer to Figure 1 The application provides a surge protection method, which includes steps 102, 104 and 106.

[0079] Step 102, obtaining output state data of the charging device.

[0080] Specifically, the charging device is a device used to output electric energy to charge the battery to be charged, which can be a charging pile or a charging base station, etc. The output state data refers to parameters related to the electric energy output by the charging device to the battery management unit of the battery to be charged. The type of the output state data is not unique, and any parameter that can be used for surge evaluation or is affected by surge to change can be used, which can be a directly measured electric parameter or a parameter calculated by further analyzing and calculating the measured parameter, and is not limited herein.

[0081] The output state data is not unique, and in one embodiment, it can be collected by configuring related detection devices between the charging device and the battery management unit, such as a state monitoring component. The specific output state data types will be different, and are not limited in particular.

[0082] At step 104, machine learning analysis is performed according to the output state data and a preset surge risk model to determine a predicted surge risk level.

[0083] Specifically, the preset surge risk model is a surge risk level prediction model established in advance according to surge risk test analysis of the same type of charging equipment and the battery to be charged. The predicted surge risk level is the risk level that will trigger a surge impact under the current charging state. The number of predicted surge risk levels will vary according to different actual scenarios, and can be configured according to actual needs.

[0084] It should be pointed out that the establishment method of the preset surge risk model is not unique, and it can be obtained by model training based on a long short-term memory (LSTM). In another embodiment, the preset surge risk model can also be obtained by training based on a GRU (Gated Recurrent Unit), a convolutional neural network model, and a recurrent neural network model. In other embodiments, it can also be obtained by training in combination with multiple different models, and the specific implementation is not limited.

[0085] At step 106, the number of surge suppressors between the accessed battery management unit and the charging equipment is dynamically adjusted according to the predicted surge risk level.

[0086] Specifically, the surge suppressor is a device that can suppress surges. The battery management unit (BMU) is a device for monitoring and managing the state of the battery, converting and transmitting the electrical energy output by the charging equipment to the battery unit for charging, and managing the charging and discharging process.

[0087] It should be pointed out that the type of surge suppressor is not unique, and it can be a TVS (Transient Voltage Suppressor). In another embodiment, it can also be a metal oxide varistor, a semiconductor discharge tube, a gas discharge tube, a silicon avalanche diode, etc. In other embodiments, it can also be a combination of the above, and the specific implementation is not limited. In order to facilitate understanding of the technical solutions of the present application, the following embodiments can be understood as a TVS for the surge suppressor.

[0088] In one embodiment, the surge suppressor can be in the form of a surge suppression array circuit, which is arranged at the front end of the battery management unit, i.e., between the charging equipment and the battery management unit. In actual scenarios, only the switching of different numbers of surge suppressors in the surge suppression array circuit needs to be controlled to achieve the adjustment of the number of accessed surge suppressors.

[0089] In one embodiment, please refer to Figure 2 , the surge suppression array circuit 10 includes a normally closed surge component 11 and at least one controllable surge branch 12, the controllable surge branch 12 is connected in parallel with the normally closed surge component 11, and the first end of the parallel connection is connected to the charging device, and the second end of the parallel connection is connected to the battery management unit. The controllable surge branch 12 includes a controllable switch and a surge suppressor connected in series, and the controllable switch is connected to the controller 30. In this way, in the case that the controllable surge branch 12 is not put into operation, the normally closed surge component 11 is always maintained in operation, and under the action of the controller, the number of controllable surge branches 12 put into operation can be changed, thereby changing the number of surge suppressors connected between the battery management unit and the charging device.

[0090] In another embodiment, the surge suppression array circuit can also include a plurality of controllable surge branches, and the number of surge suppressors connected between the battery management unit and the charging device can be changed by controlling the number of controllable surge branches put into operation.

[0091] The above scheme provides a plurality of surge suppressors in front of the battery management unit of the battery to be charged, obtains the output state data of the charging device during the charging of the charging device to the battery to be charged, inputs the preset surge risk model with the output state data for machine learning analysis to determine the predicted surge risk level of the surge risk under the current state. After that, the number of surge suppressors connected between the battery management unit and the charging device is dynamically adjusted in combination with the predicted surge risk level to suppress the output surge of the charging device. Through the scheme, the output surge of the charging device can be predicted in combination with the output state data, and the surge suppression capacity can be dynamically adjusted according to the prediction result, the surge risk can be warned in advance, and the surge suppressors are put into operation in an appropriate number. In this way, the prediction and protection of abnormal surges output by the charging device can be realized, the risk of the battery management unit suffering from electrical overstress can be greatly reduced, and the burning of the battery management unit can be alleviated.

[0092] Please refer to Figure 3 In some embodiments, step 104 includes step 302 and step 304.

[0093] Step 302: performing feature extraction analysis according to the output state data to determine the feature vector parameter.

[0094] Step 304: performing machine learning analysis according to the feature vector parameter and the preset surge risk model to determine the predicted surge risk level.

[0095] Specifically, the feature extraction analysis, that is, the analysis and calculation on the output state data, extracts the feature parameters of other charging operation states. The feature vector parameter, that is, the different feature parameters obtained through the feature extraction analysis, is fused to obtain the final state parameter. That is, the scheme of the embodiment is not to directly learn and analyze the collected output state data, but to pre-process the output state data, extract and fuse the feature vector parameters, and then learn and analyze the feature vector parameters.

[0096] The above scheme needs to extract features from the output state data after obtaining the output state data, and input the feature vector parameters into the preset surge risk model for machine learning analysis. In this way, the data type of the input can be enriched, and the accuracy of the machine learning result can be improved.

[0097] Please refer to Figure 4 In some embodiments, step 302 includes step 402 and step 404.

[0098] Step 402: performing feature extraction analysis according to the output state data to determine the initial feature vector parameter.

[0099] Step 404: performing dimension reduction analysis according to the initial feature vector parameter to determine the feature vector parameter.

[0100] Specifically, the initial feature vector parameter is the feature vector parameter preliminarily obtained after the fusion of the feature parameters extracted from the output state data. The dimension reduction analysis is the analysis of the initial feature vector parameter to reduce the data dimension of the initial feature vector parameter.

[0101] It can be understood that the dimension reduction analysis is not unique. In an embodiment, PCA (Principal Components Analysis) dimension reduction technology can be used to perform dimension reduction processing on the initial feature vector parameter. That is, the initial feature vector parameter is projected into a low-dimensional space through linear transformation while retaining the main change information in the data.

[0102] In another embodiment, other methods can also be used to reduce the dimension of the initial feature vector parameter, such as linear discriminant analysis, multidimensional scaling, etc. The specific method is not limited and can be selected according to actual needs.

[0103] The above scheme first performs feature extraction analysis to determine the initial feature vector parameter, and then performs dimension reduction analysis on the initial feature vector parameter to determine the feature vector parameter. In this way, the data storage space can be reduced, the complexity of subsequent calculation can be reduced, and the execution efficiency of surge protection can be improved.

[0104] Please refer to Figure 5In some embodiments, the output state data comprises an output current parameter, an output voltage parameter, and a noise parameter; and step 402 comprises step 502 and step 504.

[0105] Step 502, at least one of time domain analysis, frequency domain analysis, and wavelet transform analysis is performed on the output current parameter and the output voltage parameter to determine an extracted feature parameter.

[0106] Step 504, an initial feature vector parameter is determined according to the extracted feature parameter and the noise parameter.

[0107] Specifically, the output voltage parameter, i.e., the voltage value of the electric energy output by the charging device to the battery management unit, can be understood as a charging voltage; and the output current parameter, i.e., the current value of the electric energy output by the charging device to the battery management unit, can be understood as a charging current.

[0108] Time domain analysis refers to analyzing a signal in the time domain, which mainly focuses on the changes of the signal over time, such as the amplitude, waveform, and period of the signal. Frequency domain analysis is a method of analyzing a signal by converting it from the time domain to the frequency domain, which mainly focuses on the distribution and intensity of different frequency components in the signal. Wavelet transform analysis is a time-frequency analysis method that combines the characteristics of time domain and frequency domain analysis, which analyzes the characteristics of a signal at different times and frequencies by shifting and scaling the wavelet basis function. The extracted feature parameter is the parameter extracted from the output current parameter and the output voltage parameter after time domain analysis, frequency domain analysis, or wavelet transform analysis.

[0109] According to different actual needs, the output current parameter and the output voltage parameter can be analyzed by only one of time domain analysis, frequency domain analysis, and wavelet transform analysis, or by multiple ones of time domain analysis, frequency domain analysis, and wavelet transform analysis, which is not limited in particular.

[0110] The noise parameter, i.e., the number or frequency of noise events, can be understood as a noise count value. In the actual analysis process, the noise parameter can be directly input into the preset surge risk model for learning analysis, or the noise parameter can be processed and then the processed parameter is input into the preset surge risk model for learning analysis, which is not limited in particular.

[0111] For example, in an embodiment, the noise parameter (noise count value) can be combined with the sampling period to calculate (e.g., the ratio of the two), to obtain a noise pulse density, and then the noise pulse density and the extracted feature parameter are combined to determine the initial feature vector parameter.

[0112] The above scheme, in combination with the output current parameter and the output voltage parameter, at least one of time domain analysis, frequency domain analysis and wavelet conversion analysis, in combination with the noise parameter to determine the initial feature vector parameter, can effectively improve the richness of the initial feature vector parameter.

[0113] Please refer to Figure 6 In some embodiments, the output state data further includes a temperature compensation parameter and / or an aging compensation parameter, and the method further includes step 602.

[0114] Step 602, determining the initial feature vector parameter according to the extracted feature parameter, and the temperature compensation parameter and / or the aging compensation parameter.

[0115] Specifically, the temperature compensation parameter is a temperature value of a surge protection system located between the charging device and the battery management unit when the charging device charges the battery to be charged. The aging compensation parameter is a parameter representing different aging degrees of devices. In a more detailed embodiment, a metal oxide varistor (MOV) is further connected in series between the surge suppression array circuit and the charging device, and the temperature compensation parameter can be the surface temperature of the metal oxide varistor collected in real time. Correspondingly, the aging compensation parameter is the aging degree parameter of the metal oxide varistor.

[0116] The processing method of the output current parameter, the output voltage parameter and the noise parameter is as shown in the above embodiment, which will not be described here. The scheme of the present embodiment further considers the influence of the temperature compensation parameter and / or the aging compensation parameter on the surge prediction result on the basis of the output current parameter, the output voltage parameter and the noise parameter, and introduces the temperature compensation parameter and / or the aging compensation parameter for learning analysis.

[0117] It can be understood that in an embodiment, the temperature compensation parameter does not need to be further processed, but only needs to be combined with the real-time collected temperature compensation parameter, and the extracted feature parameter and the noise parameter for fusion analysis, so as to obtain the initial feature vector parameter.

[0118] The above scheme further considers the temperature compensation parameter and / or the aging compensation parameter on the basis of the output current parameter, the output voltage parameter and the noise parameter, improves the data dimension of the input preset surge risk model, and further improves the accuracy of the prediction result.

[0119] In some embodiments, the extracted feature parameter includes at least one of a voltage peak value, a current peak value, a voltage rise time, a current rise time, a voltage pulse density, a current pulse density, a voltage waveform kurtosis, a current waveform kurtosis, a voltage total harmonic distortion rate, a current total harmonic distortion rate, a voltage spectral centroid, a current spectral centroid, a voltage high-frequency energy ratio, a current high-frequency energy ratio, a voltage wavelet energy, a current wavelet energy, a voltage wavelet entropy and a current wavelet entropy.

[0120] Specifically, the voltage peak value refers to the maximum value of the voltage in a voltage cycle; the current peak value refers to the maximum value of the current in a current cycle. The voltage rise time refers to the time required for the voltage to rise from a certain threshold (such as 10%) to another threshold (such as 90%); the current rise time refers to the time required for the current to rise from a certain threshold to another threshold. The voltage pulse density refers to the number of voltage pulses per unit time, reflecting the density of pulses in the signal; the current pulse density refers to the number of current pulses per unit time. The voltage waveform kurtosis is a statistical quantity describing the sharpness of the voltage waveform, reflecting the deviation of the voltage waveform from the normal distribution; the current waveform kurtosis is a statistical quantity describing the sharpness of the current waveform, reflecting the deviation of the current waveform from the normal distribution.

[0121] The voltage total harmonic distortion (THD) is the ratio of the harmonic content to the fundamental content in the voltage signal; the current total harmonic distortion is the ratio of the harmonic content to the fundamental content in the current signal. The voltage spectral centroid is the center position of the energy distribution in the voltage signal spectrum; the current spectral centroid is the center position of the energy distribution in the current signal spectrum. The voltage high-frequency energy ratio is the ratio of the high-frequency component energy to the total energy in the voltage signal; the current high-frequency energy ratio is the ratio of the high-frequency component energy to the total energy in the current signal. The voltage wavelet energy is obtained by wavelet transform, and is the energy distribution of the voltage signal at different scales; the current wavelet energy is obtained by wavelet transform, and is the energy distribution of the current signal at different scales. The voltage wavelet entropy is the entropy value of the voltage signal based on wavelet transform; the current wavelet entropy is the entropy value of the current signal based on wavelet transform.

[0122] It should be noted that in one embodiment, after receiving the output current parameters and output voltage parameters collected and sent by the monitoring component, the controller also needs to preprocess the output current parameters and output voltage parameters to obtain normalized voltage and normalized current, and subsequent time domain analysis, frequency domain analysis and wavelet transform analysis are based on the normalized voltage and normalized current.

[0123] Specifically, the normalized voltage is analyzed. After being sampled by an ADC (Analog-to-Digital Converter), the output voltage parameter is converted into a voltage raw value to reflect the instantaneous voltage sampling value; wherein, represents the sampling time, represents the voltage raw value, represents the output voltage parameter, i.e., the real-time voltage signal, is a reference voltage value. The current raw value is represented as to reflect the instantaneous current sampling value, wherein is the output current parameter, i.e., the real-time current signal.

[0124] After pre-processing, the voltage after power frequency notch is obtained , wherein, is the half delay of power frequency cycle, the typical value is 0.02, reflecting the 50Hz signal notch delay parameter; voltage baseline , wherein is the size of the sliding window, the typical point number is 1000 (which can be set to other sizes according to actual needs), reflecting the sampling point number of baseline filtering; baseline voltage , finally the normalized voltage is obtained (obtained by analyzing multiple sampling data in one sampling period).

[0125] After power frequency notch and baseline removal, the normalized current is obtained in the same way as described above. Subsequently, time domain analysis, frequency domain analysis and wavelet transform analysis can be performed according to the normalized voltage and the normalized current .

[0126] In a more detailed embodiment, for time domain analysis:

[0127] The voltage peak value is represented as , the current peak value is represented as . The voltage rise time is the event that the voltage rises from 0.1 times the voltage peak value to 0.9 times the voltage peak value in one voltage period, that is , the current rise time is similar, which can be represented as .

[0128] The voltage pulse density can be calculated according to , the current pulse density can be calculated according to ; wherein, is the voltage differential threshold, which is a preset constant, typically 0.2 (which can also be set to other sizes); is the current differential threshold, which is also a preset constant, typically 0.15 (which can also be set to other sizes).

[0129] The voltage waveform kurtosis can be calculated according to , the current waveform kurtosis can be calculated according to , wherein, is the voltage signal mean, is the voltage signal standard deviation; is the current signal mean, The standard deviation of the current signal can be obtained by combining the normalized voltage and the normalized current, and a detailed description is omitted.

[0130] In a more detailed embodiment, for frequency domain analysis:

[0131] First, the normalized voltage and the normalized current are subjected to Fourier transform, and the complex spectrum of the voltage and the current is output, that is, and can be expressed as: , , is the Fourier transform operator, reflecting the linear transformation from the time domain to the frequency domain.

[0132] After that, the voltage total harmonic distortion can be obtained by analyzing , and the current total harmonic distortion can be obtained by analyzing ; wherein, is the highest harmonic order, reflecting the harmonic analysis order, is the harmonic frequency ( ).

[0133] The voltage spectrum center of gravity can be obtained by analyzing , and the current spectrum center of gravity can be obtained by analyzing . The voltage high-frequency energy ratio can be obtained by analyzing , and the current high-frequency energy ratio can be obtained by analyzing ; wherein, is the high-frequency analysis starting frequency, representing the lower limit of the noise frequency band; is the high-frequency analysis cutoff frequency, representing the upper limit of the noise frequency band.

[0134] In a more detailed embodiment, for wavelet transform analysis:

[0135] First, the normalized voltage and the normalized current are subjected to wavelet coefficient calculation, wherein the voltage wavelet coefficient , and the current wavelet coefficient ; wherein, is the wavelet transform operator, representing the multi-scale time-frequency analysis operator; , which belongs to the wavelet decomposition scale, assists in the decomposition level index.

[0136] After that, the voltage wavelet energy and the current wavelet energy are calculated, The voltage wavelet energy can be calculated respectively by taking values 1-6 in sequence - That is ; similarly, the current wavelet energy can be calculated respectively - That is .

[0137] The energy normalization of voltage and current is carried out respectively, and and are obtained respectively. The voltage wavelet entropy can be calculated and analyzed according to , and the current wavelet entropy can be calculated and analyzed according to .

[0138] In the above scheme, the feature parameters can be selected according to actual scene or demand, at least one of time domain analysis, frequency domain analysis and wavelet conversion analysis, which can effectively improve the adaptability of surge protection method.

[0139] The machine learning analysis mode according to the output state data and the preset surge risk model is not unique, and the specific learning analysis mode is different according to the different preset surge risk model. In order to facilitate understanding, take LSTM as an example to explain and describe.

[0140] The input feature vector parameters are analyzed based on the LSTM gating mechanism, which specifically includes:

[0141] Forget gate: , wherein is a sigmoid activation function (range belongs to 0-1), is a forget gate weight matrix, is a hidden state and the concatenated vector after splicing the input feature vector parameters , is a forget gate bias vector.

[0142] Input gate: , , wherein is an input gate weight matrix , is an input gate bias vector, is a candidate state weight matrix, is a candidate state bias vector.

[0143] Cell state update , output gate: , , wherein Biasing vector for output gate.

[0144] After that, the risk probability calculation is performed in combination with the above output:

[0145]

[0146] wherein, is the output layer weight matrix, is the hidden state matrix, is the output layer bias matrix, represents the risk probability, , and represent the probability of occurrence of different surge risk levels, and the sum of the three is 1 (taking three predicted surge risk levels as an example).

[0147] Finally, the probability dominates the decision, and the production instruction is produced according to the risk probability matrix, and the final predicted surge risk level is output: .

[0148] It should be pointed out that in one embodiment, when the risk probability is calculated by taking into account the temperature compensation parameter, the temperature compensation parameter needs to be introduced, and the same way of analysis as described above is adopted to determine the probability matrix not including the aging compensation parameter , and on the basis of , the compensation probability caused by the aging compensation parameter is introduced , and the corresponding final risk probability matrix is: wherein, , is the MOV aging factor (0-1, 0 for new product state, 1 = scrap state), and only is obtained is substituted into the above probability dominant decision, and the predicted surge risk level under this embodiment can be determined.

[0149] In some embodiments, the higher the predicted surge risk level, the more surge suppressors are connected between the battery management unit and the charging device.

[0150] Specifically, the higher the predicted surge risk level, the more serious the surge that occurs, and at this time, more surge suppressors need to be put into operation to meet the surge suppression demand.

[0151] The above scheme connects more surge suppressors in the case of a higher predicted surge risk level, and performs surge suppression in this way, so as to adapt the surge suppression capability to the predicted surge risk level and improve the surge suppression accuracy.

[0152] Please refer to Figure 7In some embodiments, step 106 comprises step 702, step 704 and step 706.

[0153] Step 702, in the case of predicting the surge risk level as the first level, controlling the first number of surge suppressors to access between the battery management unit and the charging device.

[0154] Step 704, in the case of predicting the surge risk level as the second level, controlling the second number of surge suppressors to access between the battery management unit and the charging device.

[0155] Step 706, in the case of predicting the surge risk level as the third level, controlling the third number of surge suppressors to access between the battery management unit and the charging device.

[0156] Specifically, the risk degree of the second level is higher than that of the first level, and the second number is greater than the first number; the risk degree of the third level is higher than that of the second level, and the third number is greater than the second number. For ease of understanding, in an embodiment, the first risk level can be understood as a low risk level, the second risk level as a medium risk level, and the third risk level as a high risk level.

[0157] It should be pointed out that the sizes of the first number, the second number and the third number are not unique, in an embodiment, the first number can be set to 0, and in other embodiments, the first number can also be set to 1, and the specific limitation is not limited, as long as it is lower than the second number. The size of the third number is not unique, in a more detailed embodiment, all surge suppressors can be accessed to run in the case of the third level, and in another embodiment, the third number can also be set to other sizes, and the specific limitation is not limited, as long as it is greater than the second number.

[0158] For ease of understanding, in an embodiment, taking four surge suppressors as an example, in the case of predicting the surge risk level as the first level, one surge suppressor is accessed to run; in the case of predicting the surge risk level as the second level, two or three surge suppressors are accessed to run, and in the case of predicting the surge risk level as the third level, four surge suppressors are accessed to run.

[0159] The above scheme divides the predicted surge risk level into three different levels, and accesses different numbers of surge suppressors under different levels, so that different surge protection strategies can be distinguished with fewer risk levels, and the surge protection operation reliability is improved.

[0160] Please refer to Figure 8 In some embodiments, the method further comprises step 802 and step 804.

[0161] Step 802, in the case of predicting that the surge risk level reaches the target risk level, switching the backup branch access for output state data acquisition.

[0162] Step 804, in the case of predicting that the surge risk level does not reach the target risk level, maintaining the main branch access for output state data acquisition.

[0163] Specifically, the high-frequency noise suppression capability of the backup branch is stronger than that of the main branch. The main branch is a circuit that connects the monitoring component and the surge suppression array circuit to achieve output state data acquisition in the case of the surge risk level not reaching the target risk level; the auxiliary branch is a circuit that connects the monitoring component and the surge suppression array circuit to achieve output state data acquisition in the case of the surge risk level reaching the target risk level.

[0164] The structures of the main branch and the auxiliary branch are not unique, as long as the high-frequency noise suppression capability of the auxiliary branch is stronger than that of the main branch, so that more reliable high-frequency noise suppression can be achieved under the target risk level. For example, in one embodiment, the main branch includes a connection line, and the auxiliary branch includes a filter circuit. In another embodiment, the main branch also includes a filter circuit, and the auxiliary branch includes a filter circuit with stronger filtering capability, which is not limited in detail. For example, in a more detailed embodiment, the main branch includes an LC (inductance-capacitance) filter circuit, and the auxiliary branch includes a π-type filter circuit. More specifically, the π-type filter circuit is built by a magnetic bead and a ceramic capacitor, which can be selected according to actual needs.

[0165] It can be understood that the switching implementation of the main branch and the auxiliary branch is not unique. In one embodiment, both branches can be connected to a switching switch, and the switching of the branches and the auxiliary branches can be realized by switching control of the switching switch. For example, the input end of the switching switch is connected to the surge suppression array circuit, the first output end of the switching switch is connected to the monitoring component through the main branch, the second output end of the switching switch is connected to the monitoring component through the backup branch, and the switching switch is connected to the controller. It should be noted that the type of the switching switch is not unique, which can be a relay, a single-pole double-throw switch, etc., which is not limited in detail.

[0166] The selection of the above-mentioned target risk level is not unique, and different configurations can be made according to actual needs, which is not limited in detail. For example, in one embodiment, the highest risk level in the predicted surge risk level can be taken as the target risk level; in another embodiment, a predicted surge risk level in the middle can be taken as the target risk level, such as the second level in the case of three levels, which can be configured according to actual needs.

[0167] The above scheme can collect output state data through different branch accesses, and in the case that the predicted surge risk level reaches the target risk level, the standby branch access with stronger high-frequency noise suppression capability is switched to run. In this way, the high-frequency noise suppression capability can be improved, and the main branch can run when the standby branch is damaged at the target risk level, thereby having certain redundancy capability.

[0168] Referring to Figure 9 In some embodiments, the method further includes step 902 and step 904.

[0169] Step 902, in the case that the predicted surge risk level reaches the target risk level, output a power reduction charging instruction.

[0170] Step 904, in the case that the predicted surge risk level does not reach the target risk level, maintain the current charging power charging operation.

[0171] Specifically, the power reduction charging instruction is an instruction for instructing the charging device to reduce the charging power for charging. In the case that the controller detects that the predicted surge risk level reaches the target risk level, the power reduction charging instruction can be returned to the charging device to reduce the charging power; and in the case that the predicted surge risk level does not reach the target risk level, that is, the predicted surge risk level is low, the current charging power charging is maintained to maintain the charging efficiency.

[0172] Similarly, the selection of the target risk level is not unique. In an embodiment, the highest risk level in the predicted surge risk level can be selected as the target risk level; and in another embodiment, a predicted surge risk level at an intermediate level can be selected as the target risk level, for example, in the case of three levels, the second level can be selected as the target risk level, and the specific configuration can be combined with actual needs.

[0173] The above scheme can output a power reduction charging instruction in the case that the predicted surge risk level reaches the target risk level, so as to reduce the output power of the charging device and improve the charging safety.

[0174] Referring to Figure 10 In some embodiments, the method further includes step 1002 and step 1004.

[0175] Step 1002, obtain health state monitoring information of the surge suppressor.

[0176] Step 1004, dynamically adjust the number of surge suppressors accessed between the battery management unit and the charging device according to the predicted surge risk level and the health state monitoring information.

[0177] Specifically, the health state monitoring information is information obtained by real-time monitoring and used to represent the health state of each surge suppressor. In actual scenarios, due to the differences in the running time of each surge suppressor, there are certain differences in the health state of each surge suppressor. In order to balance the life differences between each surge suppressor, or to provide surge protection when the life of the surge suppressor is exhausted, the embodiment needs to consider the health state monitoring information of the surge suppressor and dynamically adjust the access of the surge suppressor. In this way, even at the same predicted surge risk level, the same number of different surge suppressors can be accessed; or under different predicted surge risk levels, the same surge suppressor can be accessed according to the demand.

[0178] For example, in a more detailed embodiment, under different predicted surge risk levels, the controller stores corresponding protection strategies, and at the same predicted surge risk level, the accessed surge suppressor is fixed. However, considering the health state monitoring information of the surge suppressor, the protection strategy can be adjusted in the actual control process, so that different surge suppressors (with the same number) are accessed for the same predicted surge risk level.

[0179] The above scheme can also monitor the health state of the surge suppressor, and access the surge suppressor with good health state in combination with the health state monitoring information and the predicted surge risk level, to improve the reliability of surge suppression.

[0180] Please refer to Figure 11 In some embodiments, the method further includes step 112.

[0181] Step 112, in the case of meeting the forced target risk level execution condition, adjusting the access of the battery management unit and the charging device between the target risk level corresponding number of surge suppressors.

[0182] In the case of not meeting the forced target risk level execution condition, step 106 is executed.

[0183] Specifically, the forced target risk level execution condition is a condition that needs to be met when the surge protection system forces the protection strategy corresponding to the target risk level to execute the protection operation without probability decision.

[0184] Similarly, the selection of the target risk level is not unique. In one embodiment, the highest risk level in the predicted surge risk level can be selected as the target risk level. In another embodiment, the predicted surge risk level in the middle can also be selected as the target risk level. For example, in the case of three levels, the second level can be selected as the target risk level, and the actual demand can be configured.

[0185] Taking the highest risk level as an example, in an embodiment, when it is detected that the forced target risk level execution condition is met, the controller will directly enter the surge protection stage, and control the target risk level corresponding number (such as 4) of surge suppressors to be connected between the battery management unit and the charging device.

[0186] The above scheme, in the case of meeting the forced target risk level execution condition, no longer makes a decision according to the predicted surge risk level, but directly connects the target risk level corresponding number of surge suppressors to run, further improving the surge protection reliability.

[0187] In some embodiments, the forced target risk level execution condition is determined to be met in at least one of the following cases: first, the current pulse density determined according to the output state data is greater than a preset current density threshold; second, the voltage pulse density determined according to the output state data is greater than a preset voltage density threshold; third, the current change rate determined according to the output state data is greater than a preset current change threshold; and fourth, the voltage change rate determined according to the output state data is greater than a preset voltage change threshold.

[0188] Specifically, the determination methods of the current pulse density and the voltage pulse density are as shown in the above embodiments, which will not be described here. The current change rate is the rate of change of current over time, and the voltage change rate is the rate of change of voltage over time. In actual scenarios, considering that a surge usually causes a sudden change in voltage and current, at least one of the current pulse density, the voltage pulse density, the current change rate, and the voltage change rate can be combined to analyze whether the forced target risk level execution condition is met.

[0189] The sizes of the preset current density threshold, the preset voltage density threshold, the preset voltage change threshold, and the preset current change threshold are not unique. As long as it is indicated that the surge corresponding to the target risk level occurs when the threshold is reached, the specific configuration can be combined with actual scenarios.

[0190] The above scheme can combine at least one of the current pulse density, the voltage pulse density, the current change rate, and the voltage change rate to determine whether the forced target risk level execution condition is met, which has high forced target risk level execution condition identification accuracy.

[0191] In some embodiments, the method further includes updating the preset surge risk model according to the real-time acquired output state data.

[0192] Specifically, when the preset surge risk model is used for prediction analysis, the model can also be retrained in combination with the real-time acquired output state data, so as to optimize the parameters in the preset surge risk model, that is, update the preset surge risk model.

[0193] The above scheme can also be combined with output state data and a preset surge risk model to update, greatly improving the accuracy of the preset surge risk model.

[0194] In order to facilitate the understanding of the technical solutions of the present application, the present application will be explained and described in detail below in combination with more detailed embodiments.

[0195] The signal acquisition layer: real-time acquisition of the output state data of the charging device (charging pile) output to the battery to be charged at the beginning of charging, including output voltage parameters, output current parameters and noise parameters. These parameters can be collected by the monitoring component and sent to the controller.

[0196] The feature extraction layer: after the controller receives the output state data, it first performs preprocessing to obtain normalized current and normalized voltage. Then, in combination with the normalized voltage and the normalized current, time domain analysis, frequency domain analysis and wavelet transform analysis are performed to obtain the voltage peak value, current peak value, voltage rise time, current rise time, voltage pulse density, current pulse density, voltage waveform kurtosis, current waveform kurtosis, voltage total harmonic distortion, current total harmonic distortion, voltage spectral centroid, current spectral centroid, voltage high-frequency energy ratio, current high-frequency energy ratio, voltage wavelet energy, current wavelet energy, voltage wavelet entropy and current wavelet entropy.

[0197] For the noise parameter, the corresponding noise density parameter is obtained after preprocessing. After that, feature fusion analysis is performed in combination with the above-mentioned various parameters and the noise density parameter to obtain the initial feature vector parameter. The PCA dimensionality reduction analysis is performed on the initial feature vector parameter to obtain the feature vector parameter.

[0198] The voltage change rate and the current change rate can also be obtained in the feature extraction layer. At this time, the controller can analyze whether the forced target risk level (high risk) execution condition is met in combination with the current pulse density and the voltage pulse density. If the condition is met, the dynamic protection layer is directly entered, the corresponding number of surge suppressors of the high risk level is controlled to access between the battery management unit and the charging device, that is, all TVSs in the surge suppressor (TVS) array circuit are accessed to run. Furthermore, the standby branch can be switched to access for output state data acquisition, and a power reduction charging instruction is output to reduce the charging power. If the condition is not met, the subsequent machine learning layer is entered for analysis, and the protection control is performed according to the machine learning analysis result.

[0199] Machine learning layer: the controller combines the preset surge risk model and the input feature vector parameter to perform learning analysis to obtain the risk probability corresponding to different risk levels. After that, probability decision dominant analysis is performed on the obtained risk probability to determine the risk level that occupies the dominant position, and then the surge risk level prediction is realized, and the predicted surge risk level is output.

[0200] Dynamic protection layer:

[0201] In the case of low risk level, one TVS of the surge suppression array circuit is controlled to access between the battery management unit and the charging device; at this time, the main branch is maintained to collect output state data, and the current charging power is maintained to operate.

[0202] In the case of medium risk level, two TVS of the surge suppression array circuit are controlled to access between the battery management unit and the charging device; at this time, the main branch is maintained to collect output state data, and the current charging power is maintained to operate.

[0203] In the case of high risk level, four TVS of the surge suppression array circuit are controlled to access between the battery management unit and the charging device; at this time, the standby branch is switched to access to collect output state data, and a reduced power charging instruction is output to reduce the charging power.

[0204] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, there is no strict order limitation for the execution of these steps, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps. It can be understood that the steps in different embodiments can be freely combined as needed, and various non-contradictory schemes formed by the combination are within the scope of protection of the present application.

[0205] Based on the same inventive concept, the embodiments of the present application also provide a surge protection device for implementing the above-mentioned surge protection method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more surge protection device embodiments provided below can refer to the limitations of the surge protection method in the foregoing, which will not be repeated here.

[0206] Please refer to Figure 12 , the present application also provides a surge protection device, comprising a data acquisition module 122, a risk prediction module 124 and a dynamic protection module 126.

[0207] The data acquisition module 122 is configured to acquire output state data of the charging device; the risk prediction module 124 is configured to perform machine learning analysis according to the output state data and a preset surge risk model to determine a predicted surge risk level; and the dynamic protection module 126 is configured to dynamically adjust a number of surge suppressors accessed between the battery management unit and the charging device according to the predicted surge risk level.

[0208] In some embodiments, the risk prediction module 124 is further configured to perform feature extraction analysis according to the output state data to determine a feature vector parameter; and perform machine learning analysis according to the feature vector parameter and the preset surge risk model to determine the predicted surge risk level.

[0209] In some embodiments, the risk prediction module 124 is further configured to perform feature extraction analysis according to the output state data to determine an initial feature vector parameter; and perform dimension reduction analysis according to the initial feature vector parameter to determine the feature vector parameter.

[0210] In some embodiments, the risk prediction module 124 is further configured to perform at least one of time domain analysis, frequency domain analysis, and wavelet conversion analysis on the output current parameter and the output voltage parameter to determine an extracted feature parameter; and determine the initial feature vector parameter according to the extracted feature parameter and a noise parameter.

[0211] In some embodiments, the risk prediction module 124 is further configured to determine the initial feature vector parameter according to the extracted feature parameter, a temperature compensation parameter, and / or an aging compensation parameter.

[0212] In some embodiments, the dynamic protection module 126 is further configured to, in a case where the predicted surge risk level is a first level, control a first number of surge suppressors to be accessed between the battery management unit and the charging device; in a case where the predicted surge risk level is a second level, control a second number of surge suppressors to be accessed between the battery management unit and the charging device; and in a case where the predicted surge risk level is a third level, control a third number of surge suppressors to be accessed between the battery management unit and the charging device.

[0213] Referring to Figure 13 In some embodiments, the apparatus further includes a path switching module 132.

[0214] The path switching module 132 is configured to, in a case where the predicted surge risk level reaches a target risk level, switch a backup branch to be accessed for output state data acquisition; and in a case where the predicted surge risk level does not reach the target risk level, maintain a main branch to be accessed for output state data acquisition.

[0215] Referring to Figure 14 In some embodiments, the apparatus further includes a power adjustment module 142.

[0216] The power adjustment module 142 is configured to output a reduced-power charging instruction in a case where the predicted surge risk level reaches the target risk level, and maintain the current charging power in a case where the predicted surge risk level does not reach the target risk level.

[0217] In some embodiments, the dynamic protection module 126 is further configured to acquire health state monitoring information of the surge suppressor, and dynamically adjust the number of the surge suppressors connected between the battery management unit and the charging device according to the predicted surge risk level and the health state monitoring information.

[0218] In some embodiments, the dynamic protection module 126 is further configured to, in a case where a forced target risk level execution condition is met, adjust the number of the surge suppressors connected between the battery management unit and the charging device to correspond to the target risk level, and in a case where the forced target risk level execution condition is not met, perform an operation of dynamically adjusting the number of the surge suppressors connected between the battery management unit and the charging device according to the predicted surge risk level.

[0219] Referring to Figure 15 In some embodiments, the apparatus further includes a model updating module 152.

[0220] The model updating module 152 is configured to update the preset surge risk model according to the real-time acquired output state data.

[0221] The above-mentioned various modules in the surge protection apparatus can be realized in whole or in part by software, hardware, and a combination thereof. The above-mentioned various modules can be embedded in or independent of a processor in a computer device in a hardware form, or can be stored in a memory in a computer device in a software form, so as to be called and executed by a processor to perform the operations corresponding to the above-mentioned various modules.

[0222] Referring to Figure 2 The present application also provides a surge protection system, including a surge suppression array circuit 10, a monitoring assembly 20, and a controller 30. The monitoring assembly 20 is arranged between the surge suppression array circuit 10 and a battery management unit. The surge suppression array circuit 10 includes a normally closed surge assembly 11 and at least one controllable surge branch 12. The controllable surge branch 12 is connected in parallel with the normally closed surge assembly 11, and a first end formed by the parallel connection is connected to a charging device, and a second end formed by the parallel connection is connected to the battery management unit. The controllable surge branch 12 includes a controllable switch and a surge suppressor connected in series. The monitoring assembly 20 and the controllable switch are respectively connected to the controller 30. The controller 30 is configured to perform the steps of the above-mentioned surge protection method.

[0223] Specifically, the normally-closed surge assembly 11 is also a surge suppression device maintained between the access charging device and the battery management unit under different predicted surge risk levels. It can be understood that, in order to improve the operation safety, the surge detector and the normally-closed surge assembly 11 should be grounded, which is not described herein.

[0224] The monitoring assembly 20 is also a device for monitoring the output state data of the charging device, and the type is not unique, and will be different according to the different output state data. In an embodiment, the monitoring assembly 20 includes at least one of a current detector, a voltage detector (which can be a voltage dividing circuit, etc., and is not specifically limited), and a temperature detector. Further, in an embodiment, if it is necessary to enter the aging compensation parameter or the health state monitoring information of the surge suppressor, the monitoring assembly 20 can also include a device or means for detecting the aging of the device, which can be selected according to the actual needs.

[0225] The implementation of the surge protection method is shown in the above various embodiments, which is not described herein. In actual scenarios, the number of surge suppressors between the battery management unit and the charging device can be adjusted by controlling the number of conduction of the controllable surge branch 12.

[0226] Through this scheme, the output surge of the charging device can be predicted in combination with the output state data, and the surge suppression capacity can be dynamically adjusted according to the prediction result. The surge risk can be warned in advance, and the appropriate number of surge suppressors are accessed to run. In this way, the prediction and protection of the abnormal surge output of the charging device can be realized, which greatly reduces the risk of electrical overstress of the battery management unit and alleviates the occurrence of the battery management unit ablation.

[0227] Please refer to Figure 16 In some embodiments, the normally-closed surge assembly 11 includes a surge suppressor and a self-resetting fuse in series.

[0228] Specifically, the self-resetting fuse is also a polymeric positive temperature coefficient device (PPTC), which forms a conductive path under normal working conditions, so that the current passes smoothly. When overcurrent or overheating occurs in the circuit, the polymer matrix expands due to heat, causing the contact points between the conductive particles to break, and the resistance increases sharply, thereby limiting the current passing through, playing a protective role. It can automatically recover after troubleshooting.

[0229] It can be understood that, in another embodiment, the normally-closed surge assembly 11 can also only include a surge suppressor, which can specifically include one surge suppressor, or a plurality of surge suppressors in series and / or parallel, and is not specifically limited, which can be selected according to the actual needs.

[0230] The above scheme, normally closed surge component 11 in the surge suppressor, but also in series with a self-resetting fuse, through the self-resetting fuse can be in the current when the current is limited to improve the operation reliability of the surge suppression array circuit 10.

[0231] Please refer to Figure 17 In some embodiments, the system also includes a pressure sensitive resistor 40 and / or a common mode choke 50 arranged in series between the surge suppression array circuit 10 and the charging device.

[0232] Specifically, the pressure sensitive resistor 40, that is, the element whose resistance value changes with voltage, when the voltage exceeds a certain threshold, the resistance value of the pressure sensitive resistor 40 will drop sharply, thereby limiting the further rise of the voltage, playing a role in overvoltage protection. The common mode choke 50 is an inductive element used to suppress common mode noise, which generates a reverse magnetic field to offset the common mode noise, thereby reducing the impact of noise on the circuit.

[0233] In this embodiment, it can be seen that the pressure sensitive resistor 40 is connected in series at the front end of the PPTC, that is, between the PPTC and the charging device, to absorb the medium and high energy surge (usually 1KA (kiloampere) -10KA) in the protection system. Further, the common mode choke 50 can also be connected in series at the front end of the PPTC to offset the common mode noise generated.

[0234] The above scheme can connect the pressure sensitive resistor 40 and / or the common mode choke 50 in series between the surge suppression array circuit 10 and the charging device, thereby realizing the absorption of medium and high energy surges and / or the suppression of common mode interference, and further improving the operation reliability of the surge protection system.

[0235] In some embodiments, the current detector can be connected in series at the output end of the surge suppression array circuit 10, that is, between the surge suppression array circuit 10 and the battery management unit; and the voltage detector can be connected in parallel at the output end of the surge suppression array circuit 10. The temperature detector can be arranged on the heat dissipation surface of the pressure sensitive resistor 40 to monitor the temperature.

[0236] Please continue to refer to Figure 17 In some embodiments, the charging device connects the surge suppression array circuit 10 through the positive charging line (L+) and the negative charging line (L-), and the gas discharge tube 60 is also connected in parallel between the positive charging line and the negative charging line.

[0237] Specifically, the gas discharge tube 60 (GDT, Gas Discharge Tube) is a kind of high-voltage element that uses a gas ionization mechanism to achieve surge protection, and has the characteristics of large conduction capacity, high insulation resistance, small inter-electrode capacitance, etc. In the embodiment, the gas discharge tube 60 is connected in parallel between the positive charging line and the negative charging line, which can discharge super-high energy surges greater than 10KA, and provide more protection levels for battery charging.

[0238] The above scheme can also connect the gas discharge tube 60 in parallel with the charging line of the charging device and the surge suppression array circuit 10, thereby discharging super-high energy surges, and further improving the operation reliability of the surge protection system in a multi-level protection manner.

[0239] Please refer to Figure 18 In some embodiments, the system further includes a switching switch 71, a main branch 72 and a backup branch 73, the input end of the switching switch 71 is connected to the surge suppression array circuit 10, the first output end of the switching switch 71 is connected to the monitoring assembly 20 through the main branch 72, the second output end of the switching switch 71 is connected to the monitoring assembly 20 through the backup branch 73, the switching switch 71 is connected to the controller 30, and the backup branch 73 has stronger high-frequency noise suppression capability than the main branch 72.

[0240] Specifically, the main branch 72, i.e. in the case where the surge risk level does not reach the target risk level, connects the monitoring assembly 20 and the surge suppression array circuit 10 to realize the line for output state data acquisition; the auxiliary branch, i.e. in the case where the surge risk level reaches the target risk level, connects the monitoring assembly 20 and the surge suppression array circuit 10 to realize the line for output state data acquisition.

[0241] The structures of the main branch 72 and the auxiliary branch are not unique, as long as the high-frequency noise suppression capability of the auxiliary branch is stronger than that of the main branch 72, so that more reliable high-frequency noise suppression can be realized under the target risk level. For example, in an embodiment, the main branch 72 includes a connection line, and the auxiliary branch includes a filter circuit. In another embodiment, the main branch 72 also includes a filter circuit, and the auxiliary branch includes a filter circuit with stronger filtering capability, which is not limited in particular. For example, in a more detailed embodiment, the main branch 72 includes an LC (inductor-capacitor) filter circuit, and the auxiliary branch includes a π-type filter circuit. More specifically, the π-type filter circuit is built by a magnetic bead and a ceramic capacitor, which can be selected according to actual needs.

[0242] The above scheme can collect output state data through different branch access. In the case that the predicted surge risk level reaches the target risk level, the standby branch 73 with stronger high-frequency noise suppression capability is switched to access operation. In this way, the high-frequency noise suppression capability can be improved, and the main branch 72 can be kept running when the standby branch 73 is damaged at the target risk level.

[0243] The application also provides a battery including a battery management unit, a battery unit and the above surge protection system.

[0244] Specifically, the battery unit, i.e., a unit for storing electrical energy, can be a battery cell, a battery pack formed by a plurality of battery cells connected in series and / or in parallel, or a battery pack formed by a plurality of battery packs connected in series and / or in parallel, and the specific limitation is not made.

[0245] The surge protection system structure and implementation mode are shown in the above various embodiments and the accompanying drawings, and will not be repeated here. Through the scheme, the output surge of the charging device can be predicted in combination with the output state data during the battery charging process, and the surge suppression capability can be dynamically adjusted according to the prediction result, the surge risk can be warned in advance, and a corresponding number of surge suppressors can be accessed and operated. In this way, the prediction and protection of the abnormal surge output of the charging device can be realized, the risk of the battery management unit suffering from electrical overstress can be greatly reduced, and the occurrence of the battery management unit ablation can be alleviated.

[0246] The application also provides a computer device including a memory and a processor, the memory stores a computer program, and the processor implements the steps of the following surge protection method when executing the computer program.

[0247] The output state data of the charging device is obtained, machine learning analysis is performed according to the output state data and a preset surge risk model, a predicted surge risk level is determined, and the number of surge suppressors accessed between the battery management unit and the charging device is dynamically adjusted according to the predicted surge risk level.

[0248] The application also provides a computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the steps of the following surge protection method:

[0249] The output state data of the charging device is obtained, machine learning analysis is performed according to the output state data and a preset surge risk model, a predicted surge risk level is determined, and the number of surge suppressors accessed between the battery management unit and the charging device is dynamically adjusted according to the predicted surge risk level.

[0250] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the following surge protection method steps: In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the following surge protection method steps:

[0251] Obtain output state data of the charging device; perform machine learning analysis according to the output state data and a preset surge risk model to determine a predicted surge risk level; and dynamically adjust the number of surge suppressors between the battery management unit and the charging device according to the predicted surge risk level.

[0252] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the description of the present application. In particular, as long as there is no structural conflict, the technical features mentioned in each embodiment can be combined in any way. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A surge protection method, characterized by, The method comprises: acquiring output state data of a charging device; the output state data comprises output current parameters, output voltage parameters, noise parameters, temperature compensation parameters and aging compensation parameters; performing machine learning analysis according to the output state data and a preset surge risk model to determine a predicted surge risk level; the predicted surge risk level is a risk level that will cause a surge impact under a current charging state and is predicted; according to the predicted surge risk level, dynamically adjusting the number of surge suppressors connected between a battery management unit and the charging device; wherein the battery management unit and the charging device are connected through a surge suppression array circuit, the surge suppression array circuit comprises a normally closed surge component and at least one controllable surge branch, the controllable surge branch is connected in parallel with the normally closed surge component; by changing the number of controllable surge branches put into operation, the number of surge suppressors connected is changed; the higher the predicted surge risk level is, the more surge suppressors connected between the battery management unit and the charging device.

2. The surge protection method of claim 1, wherein, the machine learning analysis according to the output state data and the preset surge risk model to determine the predicted surge risk level comprises: performing feature extraction analysis according to the output state data to determine feature vector parameters; performing machine learning analysis according to the feature vector parameters and the preset surge risk model to determine the predicted surge risk level.

3. The surge protection method of claim 2, wherein, the feature extraction analysis according to the output state data to determine the feature vector parameters comprises: performing feature extraction analysis according to the output state data to determine initial feature vector parameters; performing dimension reduction analysis according to the initial feature vector parameters to determine the feature vector parameters.

4. The surge protection method of claim 3, wherein, the feature extraction analysis according to the output state data to determine the initial feature vector parameters comprises: performing at least one of time domain analysis, frequency domain analysis and wavelet conversion analysis on the output current parameters and output voltage parameters to determine extraction feature parameters; determining the initial feature vector parameters according to the extraction feature parameters and the noise parameters.

5. The surge protection method of claim 4, wherein, The method further comprises: determining the initial feature vector parameters according to the extraction feature parameters, and the temperature compensation parameters and / or the aging compensation parameters.

6. The surge protection method according to claim 4 or 5, characterized in that, the extraction feature parameters comprise at least one of voltage peak value, current peak value, voltage rise time, current rise time, voltage pulse density, current pulse density, voltage waveform kurtosis, current waveform kurtosis, voltage total harmonic distortion, current total harmonic distortion, voltage spectral centroid, current spectral centroid, voltage high-frequency energy ratio, current high-frequency energy ratio, voltage wavelet energy, current wavelet energy, voltage wavelet entropy and current wavelet entropy.

7. The surge protection method according to any one of claims 1 to 5, wherein, the dynamically adjusting the number of surge suppressors connected between the battery management unit and the charging device according to the predicted surge risk level comprises: in the case that the predicted surge risk level is a first level, controlling a first number of the surge suppressors to be connected between the battery management unit and the charging device; In the case that the predicted surge risk level is a second level, a second number of the surge suppressors are controlled to be accessed between the battery management unit and the charging device; wherein the second level is higher than the first level, and the second number is greater than the first number; In the case that the predicted surge risk level is a third level, a third number of the surge suppressors are controlled to be accessed between the battery management unit and the charging device; wherein the third level is higher than the second level, and the third number is greater than the second number.

8. The surge protection method according to any one of claims 1-5, wherein, The method further comprises: In the case that the predicted surge risk level reaches a target risk level, switching a backup branch access for output state data acquisition; In the case that the predicted surge risk level does not reach the target risk level, maintaining a main branch access for output state data acquisition; wherein the backup branch has a stronger high-frequency noise suppression capability than the main branch.

9. The surge protection method according to any one of claims 1-5, wherein, The method further comprises: In the case that the predicted surge risk level reaches a target risk level, outputting a power reduction charging instruction; In the case that the predicted surge risk level does not reach the target risk level, maintaining a current charging power charging operation.

10. The surge protection method according to any one of claims 1-5, wherein, The method further comprises: Obtaining health state monitoring information of the surge suppressors; According to the predicted surge risk level and the health state monitoring information, dynamically adjusting the number of the surge suppressors accessed between the battery management unit and the charging device.

11. The surge protection method according to any one of claims 1-5, wherein, The method further comprises: In the case that a forced target risk level execution condition is met, adjusting the number of the surge suppressors accessed between the battery management unit and the charging device corresponding to a target risk level; In the case that the forced target risk level execution condition is not met, performing the step of dynamically adjusting the number of the surge suppressors accessed between the battery management unit and the charging device according to the predicted surge risk level.

12. The surge protection method of claim 11, wherein, In the case that at least one of the following items is met, it is determined that the forced target risk level execution condition is met; First item: The current pulse density determined according to the output state data is greater than a preset current density threshold; Second item: The voltage pulse density determined according to the output state data is greater than a preset voltage density threshold; Third item: The current change rate determined according to the output state data is greater than a preset current change threshold; Fourth item: The voltage change rate determined according to the output state data is greater than a preset voltage change threshold.

13. The surge protection method according to any one of claims 1-5, wherein, The method further comprises: According to the real-time obtained output state data, updating the preset surge risk model.

14. A surge protection device, characterized by Comprise: A data acquisition module is configured to acquire output state data of a charging device; The output state data comprises output current parameters, output voltage parameters, noise parameters, temperature compensation parameters, and aging compensation parameters; A risk prediction module is configured to perform machine learning analysis according to the output state data and a preset surge risk model to determine a predicted surge risk level; the predicted surge risk level is a risk level that is predicted to cause a surge impact under a current charging state. A dynamic protection module is configured to dynamically adjust the number of surge suppressors between the battery management unit and the charging device according to the predicted surge risk level; wherein the battery management unit and the charging device are connected through a surge suppression array circuit, the surge suppression array circuit comprises a normally closed surge component and at least one controllable surge branch, the controllable surge branch is connected in parallel with the normally closed surge component; by changing the number of controllable surge branches in operation, the number of surge suppressors connected is changed; the higher the predicted surge risk level, the more surge suppressors connected between the battery management unit and the charging device.

15. A surge protection system, characterized by A surge protection system comprises a surge suppression array circuit, a monitoring component and a controller, the monitoring component is arranged between the surge suppression array circuit and the battery management unit, the surge suppression array circuit comprises a normally closed surge component and at least one controllable surge branch, the controllable surge branch is connected in parallel with the normally closed surge component, and the first end of the parallel connection is connected to the charging device, and the second end of the parallel connection is connected to the battery management unit, the controllable surge branch comprises a controllable switch and a surge suppressor connected in series, the monitoring component and the controllable switch are connected to the controller respectively, and the controller is used to execute the steps of the surge protection method according to any one of claims 1-13.

16. The surge protection system of claim 15, wherein, The normally closed surge component comprises a surge suppressor and a self-resetting fuse connected in series.

17. The surge protection system of claim 15, wherein, The system further comprises a pressure-sensitive resistor and / or a common-mode choke coil arranged in series between the surge suppression array circuit and the charging device.

18. The surge protection system of claim 15, wherein, The charging device is connected to the surge suppression array circuit through a positive charging line and a negative charging line, and a gas discharge tube is further arranged in parallel between the positive charging line and the negative charging line.

19. The surge protection system of claim 15, wherein, The system further comprises a switching switch, a main branch and a backup branch, the input end of the switching switch is connected to the surge suppression array circuit, the first output end of the switching switch is connected to the monitoring component through the main branch, the second output end of the switching switch is connected to the monitoring component through the backup branch, the switching switch is connected to the controller, and the backup branch has a stronger high-frequency noise suppression capability than the main branch.

20. A battery, characterized by A battery management system comprises a battery management unit, a battery unit and the surge protection system according to any one of claims 15-19. 21.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-20. The processor executes the computer program to implement the steps of the surge protection method according to any one of claims 1-13.

22. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the surge protection method according to any one of claims 1-13.

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