CN110190306B - Online fault diagnosis method for fuel cell system - Google Patents
Online fault diagnosis method for fuel cell system Download PDFInfo
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- CN110190306B CN110190306B CN201910481798.9A CN201910481798A CN110190306B CN 110190306 B CN110190306 B CN 110190306B CN 201910481798 A CN201910481798 A CN 201910481798A CN 110190306 B CN110190306 B CN 110190306B
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M8/00—Fuel cells; Manufacture thereof
- H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
- H01M8/04298—Processes for controlling fuel cells or fuel cell systems
- H01M8/04313—Processes for controlling fuel cells or fuel cell systems characterised by the detection or assessment of variables; characterised by the detection or assessment of failure or abnormal function
- H01M8/04664—Failure or abnormal function
- H01M8/04679—Failure or abnormal function of fuel cell stacks
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M8/00—Fuel cells; Manufacture thereof
- H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
- H01M8/04298—Processes for controlling fuel cells or fuel cell systems
- H01M8/04313—Processes for controlling fuel cells or fuel cell systems characterised by the detection or assessment of variables; characterised by the detection or assessment of failure or abnormal function
- H01M8/04664—Failure or abnormal function
- H01M8/04686—Failure or abnormal function of auxiliary devices, e.g. batteries, capacitors
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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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/30—Hydrogen technology
- Y02E60/50—Fuel cells
Abstract
The invention discloses an online fault diagnosis method for a fuel cell system, which comprises the steps of obtaining a first fault diagnosis model, wherein the first fault diagnosis model is obtained based on real-time data of a galvanic pile and a machine learning method, and the real-time data of the galvanic pile is obtained through a galvanic pile benchmark test experiment; in the operation process of the fuel cell system, acquiring real-time measured electric pile operation data, and taking the electric pile operation data as an input parameter of a first fault diagnosis model to obtain a fault diagnosis result of the electric pile; acquiring a second fault diagnosis model which comprises standard data corresponding to each auxiliary component in the fuel cell system; acquiring real-time operation data of each auxiliary component in the fuel cell system, and taking the real-time operation data as input parameters of the second fault diagnosis model to obtain fault diagnosis results of each auxiliary component; the invention is based on the existing fuel cell system, does not need to additionally increase a sensor and test equipment, does not interfere the operation of the fuel cell system, has low implementation cost and is easy to popularize.
Description
Technical Field
The invention belongs to the technical field of fuel cells, and particularly relates to an online fault diagnosis method for a fuel cell system.
Background
The fuel cell is a device for directly converting chemical energy of fuel into electric energy, has the advantages of high operating efficiency, cleanness, no pollution, low noise and the like, and is expected to replace the traditional heat engine to solve the problem of environmental pollution of an energy system. At present, fuel cells are popularized and applied in the fields of automobiles, unmanned planes, fixed power generation and the like, and have wide application prospects in the future.
The fuel cell system comprises a stack, an air supply system, a hydrogen supply system, a thermal management system and the like, and is a complex nonlinear system. Faults such as water flooding, dry membrane, gas shortage, short circuit, catalyst poisoning and the like can occur in the operation process, and the faults have great influence on the performance and the service life of the fuel cell. Therefore, the fuel cell system is subjected to online detection in the using process, the fault state of the fuel cell system is evaluated in real time, and the method is very important for guaranteeing safe and stable operation. On one hand, the fault is found in time and corresponding fault recovery measures are taken, so that more serious faults are avoided, irreversible damage caused by the fault is reduced, and the reliability of the system is improved. On the other hand, the position of the fault is accurately positioned, the fault type and the reason of the fault are determined, and the workload during maintenance can be greatly reduced.
Although various methods for diagnosing the fault of the fuel cell system have been proposed in the prior art, the method has not been able to meet the requirements of practical application, and mainly has the following problems: 1) most of them propose diagnostic methods for only one type of failure, and cannot be directly applied to actual fuel cell systems; 2) most of the methods only aim at fault diagnosis of the fuel cell stack, but in fact fault detection of key components such as an air compressor, a hydrogen circulating pump and the like is indispensable; 3) the diagnosis method is complex, the implementation cost is high, and the diagnosis method cannot be used for online fault diagnosis. For the reasons, a fault diagnosis method which can be popularized and used and is standard and uniform is not formed in the technical field of fuel cells.
Disclosure of Invention
In order to solve the problems, the invention provides an online fault diagnosis method for a fuel cell system, which can perform real-time online detection on a fuel cell stack and accurately evaluate the fault state of the fuel cell stack, has a quick and efficient diagnosis process, and can be used for a fuel cell control system or fuel cell test equipment.
In order to achieve the technical purpose and achieve the technical effects, the invention is realized by the following technical scheme:
an online fault diagnosis method for a fuel cell system, comprising the steps of:
acquiring a first fault diagnosis model, wherein the first fault diagnosis model is obtained based on real-time data of a galvanic pile and a machine learning method; the real-time data of the galvanic pile is obtained through a galvanic pile benchmark test experiment;
and in the operation process of the fuel cell system, acquiring the real-time measured electric pile operation data, and taking the electric pile operation data as the input parameters of the first fault diagnosis model so as to obtain the fault diagnosis result of the electric pile.
Preferably, the obtaining step of the first fault diagnosis model specifically includes:
acquiring real-time normal discharge working condition data and fault working condition data of the galvanic pile to form a data set;
dividing the data set into a training data subset and a validation data subset;
selecting a machine learning model;
training the machine learning model using the subset of training data;
verifying the accuracy of a machine learning model using the verification data subset;
and adjusting the structure of the machine learning model according to the verification result, and repeatedly training and verifying to finally obtain the first fault diagnosis model with the accuracy meeting the requirement.
Preferably, the learning method of the machine learning model is any one of K-nearest neighbor, bayesian network, support vector machine, artificial neural network or deep learning.
Preferably, the adjusting of the structure of the machine learning model is based on a gradient descent method, a newton method, a conjugate gradient method or a genetic algorithm.
Preferably, the fault diagnosis result of the galvanic pile comprises flooding, dry membrane, gas shortage, short circuit and/or catalyst poisoning.
Preferably, the method further comprises the steps of:
acquiring a second fault diagnosis model which comprises standard data corresponding to each auxiliary component in the fuel cell system;
and acquiring real-time operation data of each auxiliary component in the fuel cell system, and taking the real-time operation data as input parameters of the second fault diagnosis model to further acquire fault diagnosis results of each auxiliary component.
Preferably, the step of acquiring real-time operation data of the stack and/or the step of acquiring real-time operation data of each auxiliary component in the fuel cell system further comprises:
and filtering the acquired real-time operation data in real time to remove high-frequency components of the time domain data.
Preferably, the acquiring real-time operation data of each auxiliary component in the fuel cell system is used as an input parameter of the second fault diagnosis model, so as to further obtain a fault diagnosis result of each auxiliary component, and specifically includes:
acquiring real-time operation data of each auxiliary component in the fuel cell system;
and comparing the acquired real-time operation data of each auxiliary component in the fuel cell system with the existing standard data in the second fault diagnosis model, and judging whether the auxiliary component has a fault according to whether the difference value exceeds a preset threshold value.
Preferably, the auxiliary components comprise a hydrogen circulating pump, an air compressor, a heat exchanger, a water pump, a DC/DC boost converter;
for a hydrogen circulating pump, an air compressor and a water pump, standard data refer to a relation curve of flow and pressure at different rotating speeds;
for the heat exchanger, the standard data refers to a relation curve of heat exchange coefficients and flow;
for a DC/DC boost converter, the standard data refers to the efficiency versus voltage ratio.
Preferably, the real-time operation data of the electric pile comprises: current, voltage of each battery cell, gas flow of cathode and anode, inlet and outlet temperature, inlet and outlet pressure, inlet and outlet humidity, cooling water flow and/or cooling water inlet and outlet temperature; the real-time operation data of each auxiliary component comprises: the device comprises a hydrogen circulating pump, a water pump, a DC/DC boost converter, a hydrogen circulating pump, inlet and outlet pressure, flow, air compressor speed, inlet and outlet pressure, flow, heat exchanger inlet and outlet temperature, flow, water pump speed, inlet and outlet pressure, flow, DC/DC boost converter input voltage, output voltage, input power and output power.
Compared with the prior art, the invention has the beneficial effects that:
(1) the method is based on the existing fuel cell system, does not need to additionally increase a sensor and test equipment, does not interfere the operation of the fuel cell system, and has low implementation cost and easy popularization.
(2) The method can simultaneously detect various fault states of the galvanic pile and the auxiliary component in real time, and meets the actual use requirement.
(3) The method of the invention uses a machine learning method to construct the fuel cell stack fault diagnosis model, overcomes the technical difficulty caused by the nonlinear characteristic of the fuel cell stack, and has strong adaptability and higher diagnosis accuracy.
(4) The invention can accurately judge the fault type and the fault occurrence position and provides an important reference basis for fault recovery and maintenance work of the control system.
(5) The accurate and perfect fault information obtained by the invention can provide a basis for the optimal design of the fuel cell system.
Drawings
In order that the present disclosure may be more readily and clearly understood, reference is now made to the following detailed description of the present disclosure taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a schematic diagram of a condition parameter requiring real-time measurement during a fault diagnosis process according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for constructing a fuel cell stack fault diagnosis model based on machine learning according to an embodiment of the present invention;
fig. 3 is a flow chart of an online fault diagnosis method of a fuel cell system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the scope of the invention.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
Example 1
The embodiment of the invention provides an online fault diagnosis method for a fuel cell system, which comprises the following steps:
the method comprises the following steps of (1) obtaining a first fault diagnosis model, wherein the first fault diagnosis model is obtained based on real-time data of a galvanic pile and a machine learning method; the real-time data of the galvanic pile is obtained through a galvanic pile benchmark test experiment;
and (2) in the operation process of the fuel cell system, acquiring the real-time measured electric pile operation data, and taking the electric pile operation data as an input parameter of the first fault diagnosis model to further acquire a fault diagnosis result of the electric pile, wherein preferably, the fault diagnosis result of the electric pile comprises water flooding, membrane dryness, gas shortage, short circuit and/or catalyst poisoning.
In a specific implementation manner of the embodiment of the present invention, the obtaining step of the first fault diagnosis model specifically includes:
a, performing a benchmark test experiment on a fuel cell stack to obtain real-time normal discharge working condition data and fault working condition data of the stack to form a data set; in the actual process, enough data in the data set should be ensured; as shown in fig. 1, the operating condition data includes the following parameters:
firstly, current flow;
voltage;
third, monolithic voltage: i.e. the voltage of each battery cell;
anode gas state parameters: flow, inlet and outlet temperature, inlet and outlet pressure, inlet and outlet humidity;
the state parameters of the cathode gas are as follows: flow, inlet and outlet temperature, inlet and outlet pressure, inlet and outlet humidity;
cooling water state parameter: flow, inlet and outlet temperatures;
the parameters measured in real time are data which need to be acquired in real time by the fuel cell system. Therefore, the fuel cell system fault diagnosis method does not need to introduce additional sensors and measuring devices, does not interfere the operation of the fuel cell system, and has low implementation cost;
the benchmark test experiment aiming at the fuel cell stack is divided into two parts, specifically: firstly, measuring normal operation conditions under different temperature, pressure and humidity conditions; and secondly, artificially causing the failure of the electric pile, and recording the state parameters of the fuel cell system in the failure state. In the experimental process, measuring the state parameters of the fuel cell stack shown in fig. 1, and marking the fault state corresponding to each group of state parameters to form a data sample set;
b divides the data set into two parts: a part of the training process for the machine learning model, called training data subset; the other part is used for the verification process of the machine learning model and becomes a verification data subset;
c, selecting a machine learning model; preferably, the learning method of the machine learning model is any one of K-nearest neighbor, bayesian network, support vector machine, artificial neural network or deep learning, specifically referring to fig. 2; taking an artificial neural network as an example, the number of nodes and the number of layers of an input layer, a hidden layer and an output layer need to be determined, and the number of nodes of the output layer is the number of fault states;
d, training the machine learning model by using the training data subset; in the training process, the process of continuously adjusting the state of the machine learning model according to the training data subset is the process with the longest time required in the machine learning model building process. After the training process is finished, the machine learning model can well fit the fault characteristics of the fuel cell stack, and the stack fault type can be accurately calculated according to the input parameters of the training data subset; the factors influencing the accuracy of the model are mainly: whether the working condition range of the fuel cell stack benchmark test experiment is wide enough, whether the selection of the training data set is proper, and whether the type of the machine learning method is proper;
and E, verifying the accuracy of the machine learning model by using the verification data subset, namely after the training process of the machine learning model is finished, taking the state parameters of the fuel cell system of the verification data subset as input parameters, and diagnosing the fuel cell stack by using the trained machine learning model. Evaluating the accuracy of the machine learning model according to whether the diagnosis result is consistent with the fault state of the verification data set;
f, adjusting the structure of the machine learning model according to the verification result, and repeatedly training and verifying to finally obtain a first fault diagnosis model with accuracy meeting the requirement; preferably, the adjusting of the structure of the machine learning model is based on a gradient descent method, a newton method, a conjugate gradient method, or a genetic algorithm, and specifically includes:
and if the accuracy of the machine learning model in the model verification stage does not reach the expectation, the machine learning model needs to be adjusted, and iteration is repeated to obtain an accurate fuel cell stack fault diagnosis model. The adjustment is divided into three levels:
firstly, adjusting a model training method;
if the accuracy of the model cannot reach the expectation after the model training method is adjusted, adjusting the structure of the machine learning model or replacing the machine learning method;
the expected model accuracy can not be achieved through the steps, a fuel cell stack benchmark test experiment can be supplemented, and a machine learning model is trained by using more training samples.
In a preferred implementation manner of the embodiment of the present invention, after the step of acquiring the real-time operation data of the stack, the method further includes:
and filtering the acquired real-time operation data of the galvanic pile in real time, removing high-frequency data of time domain data, and preventing interference signals from influencing fault diagnosis results, wherein hardware filtering or software filtering can be adopted for implementing the filtering technology.
Example 2
Based on the same inventive concept, as shown in fig. 3, the embodiment of the present invention is different from embodiment 1 in that: the method further comprises the steps of:
acquiring a second fault diagnosis model which comprises standard data corresponding to each auxiliary component in the fuel cell system;
acquiring real-time operation data of each auxiliary component in the fuel cell system, and taking the real-time operation data as input parameters of the second fault diagnosis model so as to obtain fault diagnosis results of each auxiliary component; in practical application, the fuel cell system simultaneously monitors various state parameters of the electric pile and various auxiliary components.
In a specific implementation manner of the embodiment of the invention, the auxiliary components comprise a hydrogen circulating pump, an air compressor, a heat exchanger, a water pump and a DC/DC boost converter;
the real-time operation data of each auxiliary component comprises the following parameters: the system comprises a hydrogen circulating pump, a water pump, a DC/DC boost converter, a hydrogen pump, a water pump, a DC/DC boost converter, an output voltage, an output power and an output power;
for a hydrogen circulating pump, an air compressor and a water pump, standard data refer to a relation curve of flow and pressure at different rotating speeds; for the heat exchanger, the standard data refers to a relation curve of heat exchange coefficients and flow; for a DC/DC boost converter, the standard data refers to the efficiency versus voltage ratio curve;
the second fault diagnosis model is used as an input parameter of the second fault diagnosis model, so that fault diagnosis results of each auxiliary component are obtained, and the method specifically comprises the following steps: comparing the acquired real-time operation data of each auxiliary component in the fuel cell system with the existing standard data in the second fault diagnosis model, and judging whether the auxiliary component has a fault according to whether the difference value exceeds a preset threshold value; more specifically:
for hydrogen circulation pumps, air compressors, water pumps: inquiring a corresponding inlet-outlet pressure ratio on a standard performance curve according to the real-time rotating speed and the flow, and comparing the pressure ratio with the pressure ratio measured in real time;
for the heat exchanger: according to the real-time flow and the standard performance data difference value, obtaining a corresponding heat exchange coefficient, and comparing the heat exchange coefficient with the heat exchange coefficient measured in real time;
for a DC/DC boost converter: and according to the real-time input voltage and the output voltage, interpolating according to the standard performance data to obtain corresponding energy conversion efficiency, and comparing the energy conversion efficiency with the energy conversion efficiency measured in real time.
And when the difference value of the real-time measurement data and the standard data of each auxiliary component exceeds a preset threshold value, indicating that the auxiliary component has a fault.
In a specific implementation manner of the embodiment of the present invention, after acquiring the real-time operation data of each auxiliary component in the fuel cell system, the method further includes:
and filtering the acquired real-time operation data in real time, removing high-frequency data of time domain data, and preventing interference signals from influencing fault diagnosis results. The filtering technique may be implemented by hardware filtering or software filtering.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. An online fault diagnosis method for a fuel cell system, characterized by comprising the steps of:
acquiring a first fault diagnosis model, wherein the first fault diagnosis model is obtained based on real-time data of a galvanic pile and a machine learning method; the real-time data of the galvanic pile is obtained through a galvanic pile benchmark test experiment;
in the operation process of the fuel cell system, acquiring real-time measured electric pile operation data, and taking the electric pile operation data as an input parameter of the first fault diagnosis model so as to obtain a fault diagnosis result of the electric pile;
acquiring a second fault diagnosis model which comprises standard data corresponding to each auxiliary component in the fuel cell system;
acquiring real-time operation data of each auxiliary component in the fuel cell system, and taking the real-time operation data as input parameters of the second fault diagnosis model so as to obtain fault diagnosis results of each auxiliary component; the method specifically comprises the following steps:
acquiring real-time operation data of each auxiliary component in the fuel cell system;
comparing the acquired real-time operation data of each auxiliary component in the fuel cell system with the existing standard data in the second fault diagnosis model, and judging whether the auxiliary component has a fault according to whether the difference value exceeds a preset threshold value;
the auxiliary components comprise a hydrogen circulating pump, an air compressor, a heat exchanger, a water pump and/or a DC/DC boost converter;
for a hydrogen circulating pump, an air compressor and a water pump, standard data refer to a relation curve of flow and pressure at different rotating speeds;
for the heat exchanger, the standard data refers to a relation curve of heat exchange coefficients and flow;
for a DC/DC boost converter, the standard data refers to the efficiency versus voltage ratio.
2. An online fault diagnosis method for a fuel cell system according to claim 1, characterized in that: the obtaining step of the first fault diagnosis model specifically comprises the following steps:
acquiring real-time normal discharge working condition data and fault working condition data of the galvanic pile to form a data set;
dividing the data set into a training data subset and a validation data subset;
selecting a machine learning model;
training the machine learning model using the subset of training data;
verifying the accuracy of a machine learning model using the verification data subset;
and adjusting the structure of the machine learning model according to the verification result, and repeatedly training and verifying to finally obtain the first fault diagnosis model with the accuracy meeting the requirement.
3. An online fault diagnosis method for a fuel cell system according to claim 2, characterized in that: the learning method of the machine learning model is any one of K neighbor, Bayesian network, support vector machine, artificial neural network or deep learning.
4. An online fault diagnosis method for a fuel cell system according to claim 2, characterized in that: the process of adjusting the structure of the machine learning model is based on a gradient descent method, a Newton method, a conjugate gradient method or a genetic algorithm.
5. An online failure diagnosis method for a fuel cell system according to any one of claims 1 to 4, characterized in that: the fault diagnosis result of the galvanic pile comprises flooding, dry membrane, gas shortage, short circuit and/or catalyst poisoning.
6. An online fault diagnosis method for a fuel cell system according to claim 1, characterized in that: the step of acquiring real-time measured stack operation data and/or the step of acquiring real-time operation data of each auxiliary component in the fuel cell system further comprises the following steps:
and filtering the acquired real-time operation data in real time to remove high-frequency components of the time domain data.
7. An online fault diagnosis method for a fuel cell system according to claim 1, characterized in that: the real-time operation data of the electric pile comprises: current, voltage of each battery cell, gas flow of cathode and anode, inlet and outlet temperature, inlet and outlet pressure, inlet and outlet humidity, cooling water flow and/or cooling water inlet and outlet temperature; the real-time operation data of each auxiliary component comprises: the rotating speed, the inlet and outlet pressure and the flow rate of the hydrogen circulating pump, the rotating speed, the inlet and outlet pressure and the flow rate of the air compressor, the inlet and outlet temperature and the flow rate of the heat exchanger, the rotating speed, the inlet and outlet pressure and the flow rate of the water pump and/or the input voltage, the output voltage, the input power and the output power of the DC/DC boost converter.
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| CN110632519A (en) * | 2019-09-30 | 2019-12-31 | 潍柴动力股份有限公司 | Fuel cell fault diagnosis method and apparatus |
| CN111029624B (en) * | 2019-12-12 | 2021-06-01 | 东风汽车集团有限公司 | System and method for on-line monitoring and recovering water state of fuel cell |
| CN112993337B (en) * | 2019-12-14 | 2022-05-17 | 中国科学院大连化学物理研究所 | Water management fault diagnosis system suitable for fuel cell attenuation process |
| CN111220913B (en) * | 2020-02-24 | 2021-01-22 | 上海捷氢科技有限公司 | Diagnostic device and diagnostic method for fuel cell stack system |
| CN111765102B (en) * | 2020-05-26 | 2021-04-30 | 稳力(广东)科技有限公司 | Fault diagnosis and protection system of air compressor for fuel cell system |
| CN114094150B (en) * | 2020-06-29 | 2023-11-17 | 中国科学院大连化学物理研究所 | Machine learning-based flow battery pile optimal operation condition prediction method |
| CN111856299A (en) * | 2020-07-29 | 2020-10-30 | 中国联合网络通信集团有限公司 | Method, device and equipment for determining power supply state |
| CN112026524A (en) * | 2020-09-16 | 2020-12-04 | 浙江吉利控股集团有限公司 | Fault diagnosis and processing method and system for fuel cell vehicle |
| CN112186226B (en) * | 2020-09-24 | 2021-05-11 | 同济大学 | Fuel cell monomer gas deficiency diagnosis method |
| CN112133941B (en) * | 2020-10-10 | 2021-07-30 | 西南交通大学 | Rapid fault diagnosis method for locomotive proton exchange membrane fuel cell system |
| CN112946497A (en) * | 2020-12-04 | 2021-06-11 | 广东电网有限责任公司 | Storage battery fault diagnosis method and device based on fault injection deep learning |
| CN113561853B (en) * | 2021-06-08 | 2023-10-10 | 北京科技大学 | On-line fault diagnosis method and device for fuel cell system |
| CN113406505A (en) * | 2021-07-22 | 2021-09-17 | 中国第一汽车股份有限公司 | Method and device for predicting residual life of fuel cell |
| CN113659175B (en) * | 2021-10-19 | 2022-04-05 | 潍柴动力股份有限公司 | Self-diagnosis method and device for fuel cell stack and electronic equipment |
| CN115000456A (en) * | 2022-05-12 | 2022-09-02 | 亿创氢能源科技(张家港)有限公司 | Short-circuit test method for electric pile in fuel cell system |
| CN114843560A (en) * | 2022-05-19 | 2022-08-02 | 上海捷氢科技股份有限公司 | Method and device for diagnosing water flooding fault of fuel cell system |
| CN115476734B (en) * | 2022-09-20 | 2023-05-23 | 中国汽车工程研究院股份有限公司 | Fuzzy reasoning-based fuel cell system water management fault diagnosis method |
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