CN113581015B - Safety early warning method and device for fuel cell system - Google Patents

Safety early warning method and device for fuel cell system Download PDF

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CN113581015B
CN113581015B CN202110636368.7A CN202110636368A CN113581015B CN 113581015 B CN113581015 B CN 113581015B CN 202110636368 A CN202110636368 A CN 202110636368A CN 113581015 B CN113581015 B CN 113581015B
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vehicle
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CN113581015A (en
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洪吉超
徐晓明
孙旭东
胡松
赤骋
陈东方
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University of Science and Technology Beijing USTB
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/30—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling fuel cells
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
    • B60Q3/00—Arrangement of lighting devices for vehicle interiors; Lighting devices specially adapted for vehicle interiors
    • B60Q3/60—Arrangement of lighting devices for vehicle interiors; Lighting devices specially adapted for vehicle interiors characterised by optical aspects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
    • B60Q3/00—Arrangement of lighting devices for vehicle interiors; Lighting devices specially adapted for vehicle interiors
    • B60Q3/70—Arrangement of lighting devices for vehicle interiors; Lighting devices specially adapted for vehicle interiors characterised by the purpose
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
    • B60Q3/00—Arrangement of lighting devices for vehicle interiors; Lighting devices specially adapted for vehicle interiors
    • B60Q3/80—Circuits; Control arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/40—Application of hydrogen technology to transportation, e.g. using fuel cells

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Abstract

The application discloses a safety pre-warning method and a device of a fuel cell system, wherein the method comprises the following steps: acquiring vehicle data of a vehicle, wherein the vehicle data comprises charging data and/or running data; carrying out normalized discrete wavelet decomposition processing on the vehicle data to obtain charging abnormal data and/or operation abnormal data; judging whether the charging abnormal data meets a risk condition and/or judging whether the operation abnormal data meets an early warning condition; when the risk condition is met, judging that the vehicle has risk, controlling the vehicle to stop charging and simultaneously carrying out charging risk prompt, and/or when the early warning condition is met, generating risk prompt information and/or safety information, and concurrently sending the risk prompt information and/or the safety information value to preset a terminal. The method realizes the online identification and the accurate positioning of early risk signals and realizes the multi-stage risk early warning for the real vehicle fuel cell system.

Description

Safety early warning method and device for fuel cell system
Technical Field
The present application relates to the field of fuel cell technologies, and in particular, to a safety early warning method and apparatus for a fuel cell system.
Background
In order to prevent serious faults and optimize the maintenance period of the battery, accurate early prediction and early warning of the possible battery safety risk are extremely important for ensuring the running safety of the vehicle and the personal safety of drivers and passengers.
The related art mainly comprises the following methods: (1) Based on laboratory data and battery online operation data, estimating SOH (State Of Health) Of the battery by adopting an SVM (Support Vector Machine ) method, obtaining variation trend Of parameters such as internal resistance Of the battery by a knowledge-based method, predicting possible safety risk Of the battery by an experience-based method, and giving out real-time Health State Of the battery; (2) Through developing the safety risk early warning research of the machine learning type fuel cell system. The sample entropy of the short voltage sequence is used as an effective characteristic of battery capacity loss, an advanced sparse Bayesian prediction model is adopted to obtain a basic corresponding relation between the capacity loss and the sample entropy, and the battery safety risk can be predicted and early-warned by combining a guided sampling concept and SBPM; (3) Common fault risks such as SOC (state of charge) jump, single overvoltage, single undervoltage, single overtemperature, overlarge pressure difference, overlarge temperature difference and the like of the fuel cell are analyzed in detail from two layers of short-term safety early warning and long-term health early warning, fault trees of single overvoltage, single overtemperature and SOC jump are provided, and root causes and early warning methods of the safety risks are analyzed.
However, since the actual vehicle operation is affected by environmental stress, aging stress, driving behavior, dynamic load and other complex stresses, the early risk signal extraction difficulty is high, and the online identification and accurate positioning of the early risk signal are difficult to realize by the method in the related art, the multistage risk early warning strategy for the actual vehicle fuel cell system is still to be perfected.
Disclosure of Invention
The present application aims to solve at least one of the technical problems in the related art to some extent.
Therefore, an object of the present application is to provide a safety early warning method for a fuel cell system, which realizes on-line identification and accurate positioning of early risk signals, and realizes multi-stage risk early warning for a real vehicle fuel cell system.
Another object of the present application is to provide a safety precaution device for a fuel cell system.
In order to achieve the above objective, an embodiment of an aspect of the present application provides a safety pre-warning method for a fuel cell system, including the following steps: acquiring vehicle data of a vehicle, wherein the vehicle data comprises charging data and/or running data; carrying out normalized discrete wavelet decomposition processing on the vehicle data to obtain charging abnormal data and/or operation abnormal data; judging whether the charging abnormal data meets a risk condition and/or judging whether the operation abnormal data meets an early warning condition; when the risk condition is met, judging that the vehicle has risk, controlling the vehicle to stop charging and simultaneously carrying out charging risk prompt, and/or when the early warning condition is met, generating risk prompt information and/or safety information, and sending the risk prompt information and/or safety information value to a preset terminal.
According to the safety early warning method of the fuel cell system, disclosed by the embodiment of the application, the early risk signals are subjected to multi-scale screening and amplification extraction based on the improved multi-scale entropy method, the early risk signals are accurately identified and positioned by the normalized discrete wavelet decomposition method, the online identification and the accurate positioning of the early risk signals are realized, and the multi-stage risk early warning for the real-vehicle fuel cell system is realized.
In addition, the safety precaution method of the fuel cell system according to the above embodiment of the present application may further have the following additional technical features:
further, in an embodiment of the present application, the performing normalized discrete wavelet decomposition processing on the vehicle data to obtain charging anomaly data and/or operation anomaly data includes: the high-frequency signals after the vehicle data are decomposed are obtained through a high-pass filter; abrupt data having time domain characteristics of early risk signals are identified from the high frequency signals.
Further, in an embodiment of the present application, the performing normalized discrete wavelet decomposition processing on the vehicle data to obtain charging anomaly data and/or operation anomaly data further includes: and carrying out secondary inspection on the vehicle data by utilizing a multi-scale entropy method, searching for data with gradual change characteristics, and analyzing the disturbance degree of the data with gradual change characteristics.
Further, in one embodiment of the present application, the method further includes: and generating a corresponding reminding mode according to the charging abnormal data and/or the difference value between the operation abnormal data and the corresponding risk condition and early warning condition, wherein the reminding mode comprises the strength of reminding action and the color identification of a reminding signal.
Further, in one embodiment of the present application, the risk condition includes a first safety threshold, and the pre-warning condition includes a second safety threshold and a third safety threshold, wherein the third safety threshold is greater than the second safety threshold.
In order to achieve the above object, another embodiment of the present application provides a safety precaution device of a fuel cell system, including: the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring vehicle data of a vehicle, and the vehicle data comprise charging data and/or running data; the processing module is used for carrying out normalized discrete wavelet decomposition processing on the vehicle data to obtain charging abnormal data and/or operation abnormal data; the judging module is used for judging whether the charging abnormal data meets a risk condition and/or judging whether the operation abnormal data meets an early warning condition; and the control module is used for judging that the vehicle has risk when the risk condition is met, controlling the vehicle to stop charging and simultaneously carrying out charging risk prompt, and/or generating risk prompt information and/or safety information and sending the risk prompt information and/or safety information value to a preset terminal when the early warning condition is met.
According to the safety early warning device of the fuel cell system, disclosed by the embodiment of the application, the early risk signals are subjected to multi-scale screening and amplification extraction based on the improved multi-scale entropy method, the early risk signals are accurately identified and positioned by the normalized discrete wavelet decomposition method, the online identification and the accurate positioning of the early risk signals are realized, and the multi-stage risk early warning for the real-vehicle fuel cell system is realized.
In addition, the safety precaution device of the fuel cell system according to the above embodiment of the present application may further have the following additional technical features:
further, in an embodiment of the present application, the processing module is specifically configured to: the high-frequency signals after the vehicle data are decomposed are obtained through a high-pass filter; abrupt data having time domain characteristics of early risk signals are identified from the high frequency signals.
Further, in an embodiment of the present application, the processing module is further configured to: and carrying out secondary inspection on the vehicle data by utilizing a multi-scale entropy method, searching for data with gradual change characteristics, and analyzing the disturbance degree of the data with gradual change characteristics.
Further, in one embodiment of the present application, the method further includes: the generation module is used for generating a corresponding reminding mode according to the charging abnormal data and/or the difference value between the operation abnormal data and the corresponding risk condition and early warning condition, wherein the reminding mode comprises the strength of reminding action and the color identification of a reminding signal.
Further, in one embodiment of the present application, the risk condition includes a first safety threshold, and the pre-warning condition includes a second safety threshold and a third safety threshold, wherein the third safety threshold is greater than the second safety threshold.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a flowchart of a safety precaution method of a fuel cell system according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a discrete wavelet decomposition process according to the present application;
FIG. 3 is a block schematic diagram of a safety precaution system of a fuel cell system in accordance with the present application;
FIG. 4 is a flow chart of a method of safety precaution for a fuel cell system in accordance with one embodiment of the present application;
fig. 5 is a block diagram of a safety precaution device of a fuel cell system according to an embodiment of the application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.
The safety precaution method and device of the fuel cell system according to the embodiment of the application are described below with reference to the accompanying drawings, and the safety precaution method of the fuel cell system according to the embodiment of the application will be described first.
Fig. 1 is a flow chart of a safety precaution method of a fuel cell system according to an embodiment of the present application.
As shown in fig. 1, the safety precaution method of the fuel cell system includes:
in step S101, vehicle data of a vehicle is acquired, wherein the vehicle data includes charging data and/or operation data.
In step S102, the vehicle data is subjected to normalized discrete wavelet decomposition processing to obtain charging abnormality data and/or operation abnormality data.
It should be appreciated that discrete wavelet decomposition may use wavelet functions (wavelet functions) and scale functions (scale functions) to analyze high frequency signals and low frequency signals, i.e., high pass filters and low pass filters, respectively. The decomposition process is as follows:
1) Passing the signal through a half-band low-pass filter with an impulse response eliminates the low frequency portion of the signal, degrading the signal resolution by half.
2) Downsampling is performed according to the Nyquist theorem, one reject sample point is spaced, half of the sample points are left on the signal, and the scale is doubled. (the filtering operation does not affect the scale of the signal) high pass filtering this half.
3) Further decomposing, the result of the high-pass filter is divided into two again, and high-pass filtering and low-pass filtering are performed.
As one possible implementation manner, in one embodiment of the present application, the normalizing discrete wavelet decomposition processing is performed on the vehicle data to obtain charging anomaly data and/or operation anomaly data, including: the high-frequency signals after the vehicle data are decomposed are obtained through a high-pass filter; abrupt data having time domain characteristics of early risk signals are identified from the high frequency signals.
Specifically, the embodiment of the application can collect the real vehicle operation data and perform safety early warning and fault diagnosis through discrete wavelet decomposition.
Specifically, the embodiment of the application can carry out discrete wavelet decomposition on the signal, the process mainly searches for data with abrupt change, and the decomposed high-frequency signal is obtained through a high-pass filter, so that the time domain characteristic of the early risk signal is obtained.
As another possible implementation manner, in one embodiment of the present application, the normalized discrete wavelet decomposition processing is performed on the vehicle data to obtain charging anomaly data and/or operation anomaly data, and further includes: and (3) carrying out secondary inspection on the vehicle data by utilizing a multi-scale entropy method, searching for data with gradual change characteristics, and analyzing the disturbance degree of the data with gradual change characteristics.
Specifically, if mutation data cannot be found, the embodiment of the application can carry out secondary inspection by utilizing a multi-scale entropy method, and the process mainly aims at finding data with gradual change and analyzing the data disorder degree.
For ease of understanding, the discrete wavelet decomposition (Normalized discrete wavelet decomposition, NDWD) algorithm is described in detail below in conjunction with fig. 2.
Specifically, the core of discrete wavelet transforms is: the signals of different frequencies are analyzed by filters of different frequencies, mainly a high-pass filter and a low-pass filter. Resolution of the signal: the information richness of the signal bearing is measured, the higher the sampling rate is, the higher the resolution is, and the opposite is. The DWT analyzes a high frequency signal and a low frequency signal, i.e., a high pass filter and a low pass filter, respectively, using a wavelet function (wavelet function) and a scale function (scale function).
DWT decomposition process:
(1) The signal x n is passed through a half-band low-pass filter with an impulse response h n, which rejects the signal at a frequency lower than p/2 (the highest frequency of the signal is p), and the signal resolution drops by half.
() Downsampling is performed according to the Nyquist theorem, one reject sample point is spaced, half of the sample points are left on the signal, and the scale is doubled. (the filtering operation does not affect the scale of the signal) high pass filtering this half. 3. This is the first stage of decomposition, and if further decomposition is to be performed, the result of the high-pass filter is again split into two, high-pass filtering and low-pass filtering.
It can be seen that the number of decomposition steps is no more than the power of 2 to the n of the signal length. Differences from fourier transforms: the time position information of the frequency is preserved.
Further, the data processing using a discrete wavelet decomposition algorithm includes the steps of:
(1) Determining input network parameters: the parameter data of the fuel cell system is used for determining the number of input network types and the number of output network types;
(2) Discrete wavelet transform: performing discrete wavelet transformation on the parameter data, selecting basic wavelet types according to the parameter data, and obtaining a de-noised training sample after the discrete wavelet transformation;
(3) Training the extreme learning machine neural network: determining a training sample and a test sample, randomly selecting the deviation between the input weight and the hidden layer by using an extreme learning machine, calculating the hidden layer output matrix and the output weight, and predicting and outputting a predicted result by the inverse process of the output weight obtained by an evolutionary algorithm.
Wherein, add the evolutionary algorithm in the extreme learning machine, including the step: firstly, randomly selecting the deviation of input weights and hidden layers as a first generation population; calculating the fitness of each individual in the population; carrying out fitness evaluation, outputting an individual corresponding to the fitness meeting the stopping standard as a prediction result, and if the fitness is not meeting the stopping standard, carrying out the fitness evaluation of the next individual; and finally outputting all the prediction results after the optimization is finished.
And when the fitness of the next individual is not satisfied, carrying out mutation, crossover and selection on the fitness of the individual, and reserving the high-quality individual in the fitness to the next generation. The evolution algorithm carries out mutation, crossover and selection processes on the primary population by optimizing two parameters, namely randomly selected input weight and hidden layer deviation, so that individuals with better adaptability can be reserved to the next generation. Meanwhile, through discrete wavelet transformation, the data characteristic information can be better obtained, and the neural network of the extreme learning machine can be better established, so that the prediction precision and the prediction time are further improved.
In step S103, it is determined whether the charging abnormality data satisfies a risk condition, and/or whether the operation abnormality data satisfies an early warning condition.
Further, in one embodiment of the present application, the risk condition includes a first safety threshold, and the pre-warning condition includes a second safety threshold and a third safety threshold, wherein the third safety threshold is greater than the second safety threshold.
Further, in one embodiment of the present application, the early warning condition is to determine that the data disorder reaches the multi-level threshold.
In step S104, when the risk condition is satisfied, it is determined that the vehicle has a risk, and charging risk prompt is performed while the vehicle is controlled to stop charging, and/or when the early warning condition is satisfied, risk prompt information and/or safety information is generated, and a terminal is preset by using the risk prompt information and/or the safety information value.
Specifically, for the charging data, if the absolute value of the charging data of any one single battery is greater than or equal to 0.015, determining that the fuel cell system is abnormal in the charging and discharging process, stopping charging and checking the battery by a contact person; otherwise, the fuel cell system is in a safe state, and the vehicle can normally run.
For the operation data, if the operation data of any single battery is more than or equal to 0.8, performing primary alarm, immediately stopping and getting off the fuel cell system at high risk of thermal runaway, and making a call to wait for rescue; if the operation data of any single battery is more than or equal to 0.6 and less than 0.8, performing secondary alarm, wherein the fuel cell system has potential risk of thermal runaway, driving to a safe place for stopping, and making a call to contact personnel to check the battery; if the operation data of all the unit cells is less than 0.6, the fuel cell system is in a safe state and the vehicle can normally run.
Further, in one embodiment of the present application, the method further includes: and generating a corresponding reminding mode according to the charging abnormal data and/or the difference value between the running abnormal data and the corresponding risk condition and early warning condition, wherein the reminding mode comprises the strength of reminding action and the color identification of a reminding signal.
For example, the reminding of different conditions can be realized through the color of an LED (light-emitting diode), the LED is normally green, the secondary early warning LED is yellow, and the primary early warning LED is red. The abnormality risk factor a in the running state of the vehicle D (i.e., operational anomaly data) can be obtained by the following formula:
running state abnormality risk coefficient:
A D ＝DWD d_nor -DWD d_ave ；
wherein DWDd is a discrete wavelet value of the running state monomer voltage, and DWD d_min A discrete wavelet minimum value for the cell voltage; DWD (discrete wavelet transform) d_max Is the discrete wavelet maximum of the cell voltage; DWD (discrete wavelet transform) d_nor A discrete wavelet minimum value for the monomer voltage after normalization; DWD (discrete wavelet transform) d_ave Is the discrete wavelet average value of the monomer voltage; a is that D Is an abnormal risk coefficient in the running state of the vehicle.
Abnormal risk coefficient A in vehicle state of charge C (i.e., charge abnormality data) can be obtained by the following formula:
A C ＝DWD c -DWD c_ave ；
wherein, DWD c For the dispersion of the cell voltageWavelet value, DWD c_ave Is the discrete wavelet average of the cell voltage.
In order to enable those skilled in the art to further understand the safety precaution method of the fuel cell system according to the embodiment of the present application, the following detailed description is provided with reference to specific embodiments.
As shown in fig. 3, the early warning system related to the safety early warning method of the fuel cell system according to the embodiment of the present application mainly includes: fuel cell systems, battery management systems, and multi-stage early warning systems. Wherein the fuel cell system includes: fuel cell 1 … … n; the battery management system includes: the system comprises an acquisition module, an equalization module and a main control module, wherein the multistage early warning system comprises: LED indicator lights (green, yellow, red) and a buzzer; the acquisition module comprises: voltage sensor, current sensor and temperature sensor.
Further, as shown in fig. 4, fig. 4 is a flowchart of a safety precaution method of the fuel cell system according to an embodiment of the present application.
As can be seen from fig. 4, in the embodiment of the present application, vehicle data may be obtained in real time, and divided into charging data and operation data (data generated during running and standing is operation data, data generated during charging is charging data), and charging data and operation data are respectively processed by a normalized discrete wavelet decomposition method, so as to obtain charging anomaly data and operation anomaly data; comparing the abnormal charging data with a first preset threshold value, and judging whether the fuel cell system has risk or not; and comparing the abnormal operation data with a second preset threshold value and a third preset threshold value to generate corresponding risk prompt information or safety information. Wherein, any monomer: any one battery can meet the condition; all monomers: all batteries meet the conditions.
According to the safety early warning method of the fuel cell system, disclosed by the embodiment of the application, the early risk signals are subjected to multi-scale screening and amplification extraction based on the improved multi-scale entropy method, the early risk signals are accurately identified and positioned by the normalized discrete wavelet decomposition method, the online identification and the accurate positioning of the early risk signals are realized, and the multi-stage risk early warning for the real-vehicle fuel cell system is realized.
Next, a safety precaution device of a fuel cell system according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 5 is a block diagram of a safety precaution device of a fuel cell system according to an embodiment of the present application.
As shown in fig. 5, the safety precaution device 10 of the fuel cell system includes: the device comprises an acquisition module 100, a processing module 200, a judging module 300 and a control module 400.
The acquiring module 100 is configured to acquire vehicle data of a vehicle, where the vehicle data includes charging data and/or operation data; the processing module 200 is used for carrying out normalized discrete wavelet decomposition processing on the vehicle data to obtain charging abnormal data and/or operation abnormal data; the judging module 300 is configured to judge whether the charging abnormal data meets a risk condition and/or judge whether the operation abnormal data meets an early warning condition; the control module 400 is configured to determine that there is a risk in the vehicle when the risk condition is satisfied, control the vehicle to stop charging and simultaneously perform charging risk prompt, and/or generate risk prompt information and/or safety information when the early warning condition is satisfied, and send the risk prompt information and/or the safety information value to a preset terminal.
Further, in one embodiment of the present application, the processing module 200 is specifically configured to: the high-frequency signals after the vehicle data are decomposed are obtained through a high-pass filter; abrupt data having time domain characteristics of early risk signals are identified from the high frequency signals.
Further, in one embodiment of the present application, the processing module 200 is further configured to: and (3) carrying out secondary inspection on the vehicle data by utilizing a multi-scale entropy method, searching for data with gradual change characteristics, and analyzing the disturbance degree of the data with gradual change characteristics.
Further, in one embodiment of the present application, the method further includes: the generation module is used for generating a corresponding reminding mode according to the charging abnormal data and/or the difference value between the operation abnormal data and the corresponding risk condition and early warning condition, wherein the reminding mode comprises the strength of reminding action and the color identification of a reminding signal.
Further, in one embodiment of the present application, the risk condition includes a first safety threshold, and the pre-warning condition includes a second safety threshold and a third safety threshold, wherein the third safety threshold is greater than the second safety threshold.
It should be noted that the foregoing explanation of the embodiment of the safety warning method of the fuel cell system is also applicable to the safety warning device of the fuel cell system of this embodiment, and will not be repeated here.
According to the safety early warning device of the fuel cell system, disclosed by the embodiment of the application, the early risk signals are subjected to multi-scale screening and amplification extraction based on the improved multi-scale entropy method, the early risk signals are accurately identified and positioned by the normalized discrete wavelet decomposition method, the online identification and the accurate positioning of the early risk signals are realized, and the multi-stage risk early warning for the real-vehicle fuel cell system is realized.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.

Claims (2)

1. A safety precaution method of a fuel cell system, comprising the steps of:
acquiring vehicle data of a vehicle, wherein the vehicle data comprises charging data and/or running data;
carrying out normalized discrete wavelet decomposition processing on the vehicle data to obtain charging abnormal data and/or operation abnormal data;
judging whether the charging abnormal data meets a risk condition and/or judging whether the operation abnormal data meets an early warning condition; and
when the risk condition is met, judging that the vehicle has risk, controlling the vehicle to stop charging and simultaneously carrying out charging risk prompt, and/or when the early warning condition is met, generating risk prompt information and/or safety information, and sending the risk prompt information and/or safety information value to a preset terminal;
the normalizing discrete wavelet decomposition processing is performed on the vehicle data to obtain charging abnormal data and/or operation abnormal data, and the normalizing discrete wavelet decomposition processing comprises the following steps: the high-frequency signals after the vehicle data are decomposed are obtained through a high-pass filter; identifying abrupt data having time domain characteristics of an early risk signal from the high frequency signal;
the method comprises the steps of carrying out normalized discrete wavelet decomposition processing on the vehicle data to obtain charging abnormal data and/or operation abnormal data, and further comprising: performing secondary inspection on the vehicle data by using a multi-scale entropy method, searching data with gradual change characteristics, and analyzing the disturbance degree of the data with gradual change characteristics;
generating a corresponding reminding mode according to the charging abnormal data and/or the difference value between the operation abnormal data and the corresponding risk condition and early warning condition, wherein the reminding mode comprises the strength of reminding action and the color mark of a reminding signal;
the risk condition comprises a first safety threshold, and the early warning condition comprises a second safety threshold and a third safety threshold, wherein the third safety threshold is larger than the second safety threshold.
2. A safety precaution device for a fuel cell system, comprising:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring vehicle data of a vehicle, and the vehicle data comprise charging data and/or running data;
the processing module is used for carrying out normalized discrete wavelet decomposition processing on the vehicle data to obtain charging abnormal data and/or operation abnormal data;
the judging module is used for judging whether the charging abnormal data meets a risk condition and/or judging whether the operation abnormal data meets an early warning condition; and
the control module is used for judging that the vehicle has risk when the risk condition is met, controlling the vehicle to stop charging and simultaneously carrying out charging risk prompt, and/or generating risk prompt information and/or safety information and sending the risk prompt information and/or safety information value preset terminal when the early warning condition is met;
the processing module is specifically configured to: the high-frequency signals after the vehicle data are decomposed are obtained through a high-pass filter; identifying abrupt data having time domain characteristics of an early risk signal from the high frequency signal;
the processing module is further configured to: performing secondary inspection on the vehicle data by using a multi-scale entropy method, searching data with gradual change characteristics, and analyzing the disturbance degree of the data with gradual change characteristics;
the generation module is used for generating a corresponding reminding mode according to the charging abnormal data and/or the difference value between the operation abnormal data and the corresponding risk condition and early warning condition, wherein the reminding mode comprises the strength of reminding action and the color mark of a reminding signal;
the risk condition comprises a first safety threshold, and the early warning condition comprises a second safety threshold and a third safety threshold, wherein the third safety threshold is larger than the second safety threshold.
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