CN114614056A - Fuel cell system based on distributed edge calculation - Google Patents
Fuel cell system based on distributed edge calculation Download PDFInfo
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- CN114614056A CN114614056A CN202210512515.4A CN202210512515A CN114614056A CN 114614056 A CN114614056 A CN 114614056A CN 202210512515 A CN202210512515 A CN 202210512515A CN 114614056 A CN114614056 A CN 114614056A
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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
- H01M8/04992—Processes for controlling fuel cells or fuel cell systems characterised by the implementation of mathematical or computational algorithms, e.g. feedback control loops, fuzzy logic, neural networks or artificial intelligence
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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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Abstract
The invention discloses a fuel cell system based on distributed edge calculation, which comprises: a fuel cell stack, an edge fuel cell system, and a central computing system; the edge fuel cell system acquires the operation data of a plurality of fuel cells in the fuel cell group and uploads the acquired operation data to the central computing system; the central computing system integrates, excavates and learns the operation data and transmits the learning result to the edge fuel cell system, and the edge fuel cell system combines the operation state of the fuel cell set and the learning result to realize the optimization control or intervention adjustment of the plurality of fuel cells; and the central computing system performs version optimization upgrading on the edge fuel cell system. The edge cell system close to the data source is fused with the central computing system, so that the data is utilized to the maximum extent, the task of overall computational power is reduced, the heavy computational power is replaced by an artificial intelligent learning and fitting mode, and the problem that the computational power of the fuel cell system is insufficient is solved.
Description
Technical Field
The invention relates to the technical field of fuel cell systems, in particular to a fuel cell system based on distributed edge calculation.
Background
The fuel cell system is complex to control, and needs to be optimally controlled in the process of outputting voltage by the fuel cell.
The existing method for optimizing and controlling the fuel cell system comprises the following steps: and directly calculating the fuel cell stack by using the big data and the short-time high-frequency acquired data. The problem with this way of optimizing control is that: 1. the running data is not integrated, mined and learned, so that the computing power is insufficient, and the computing power cost is high; 2. the running data cannot be utilized to the maximum extent, so that the accuracy of optimization control is low; 3. the computational task is heavy, and the optimization control time has time delay, so that the safety of the fuel cell stack cannot be guaranteed; 4. the consistency of the output voltage of the fuel cell under the current control parameter in the operating state cannot be judged according to the calculation result, so that the output capacity of the fuel cell system is reduced.
Therefore, there is a need for an improved fuel cell system in the prior art to solve the above-mentioned problems.
Disclosure of Invention
The invention overcomes the defects of the prior art, provides a fuel cell system based on distributed edge calculation, and aims to solve the problems that the calculation force of the edge fuel cell system is insufficient when calculating a fuel cell group, and the operation data cannot be utilized to the maximum extent to optimize the fuel cell group, improve the consistency of the output voltage of the edge fuel cell system for optimizing the single fuel cell by utilizing the operation data, and solve the problem that how the edge fuel cell system ensures the safety of the fuel cell group.
In order to achieve the purpose, the invention adopts the technical scheme that: a distributed edge computation based fuel cell system comprising: a fuel cell stack, an edge fuel cell system, and a central computing system, wherein,
the edge fuel cell system collects the operation data of a plurality of fuel cells in the fuel cell group and uploads the collected operation data to the central computing system;
the central computing system integrates, excavates and learns the operation data and transmits a learning result to the edge fuel cell system, and the edge fuel cell system combines the operation state of the fuel cell stack and the learning result to realize the optimization control or intervention adjustment of a plurality of fuel cells;
and the central computing system performs version optimization upgrading on the edge fuel cell system.
In a preferred embodiment of the present invention, the edge fuel cell system comprises an edge computer, a data acquisition module, a data transmission module, a system optimization module and a system control module;
the edge computer has the functions of issuing acquired data, transmitting data and controlling instructions to the fuel cell;
The data acquisition module receives an instruction of the edge computer processor and acquires the operating data of a plurality of fuel cells in the fuel cell pack in real time;
the data transmission module receives an instruction of the edge computer processor, and the data acquisition module acquires the operating data and uploads the operating data to the central computing system; the data transmission module receives the learning result and the version optimization upgrade of the central computing system and uploads the learning result and the version optimization upgrade to the edge computing processor;
the system optimization module receives an instruction of the edge computer, realizes the optimization of the fuel cells according to the running state of the cells and the learning result, and sends the optimization result to the system control module;
and the system control module receives the instruction of the edge computer processor and the optimization result of the system optimization module and sends the instruction and the optimization result to the controllers of the fuel cells.
In a preferred embodiment of the present invention, the central computing system comprises a data module, an intelligent learning module and a version control module;
the data module realizes the receiving and sending of the operation data, and the data module carries out integrated management on the operation data and stores the system version of the edge fuel cell;
The intelligent learning module receives the operating data of the data module, learns the operating data, realizes real-time modeling and simulation of the fuel cell stack, and obtains a fitting model;
and the version control module receives the learning result of the intelligent learning module, transmits the system version to the edge fuel cell system according to the learning result, and performs optimization and upgrading.
In a preferred embodiment of the present invention, the intelligent learning module performs modeling and simulation by combining with an artificial intelligence algorithm, and performs regression learning by using a neural network tool to obtain a fitting model.
In a preferred embodiment of the present invention, the operation data is data of the temperature, the intake air flow rate or pressure, the opening degree signal and the single fuel cell voltage of the fuel cell stack under the current control parameters.
In a preferred embodiment of the present invention, the data acquisition module is provided with a plurality of monitoring units, and the monitoring units are a temperature monitoring unit, a throttle opening monitoring unit, a flow monitoring unit, a pressure monitoring unit and a single fuel cell voltage monitoring unit.
In a preferred embodiment of the present invention, the system optimization module optimizes the voltage uniformity of the fuel cells by using the learning result, and sends the optimized result to the corresponding fuel cell through the system control module.
In a preferred embodiment of the present invention, the fitted model is a model of a fitted relation between the operating states of the individual fuel cells and the fuel cell stack.
In a preferred embodiment of the present invention, the edge computing processor predicts the safety of the fuel cell stack according to the learning result, and when the safety coefficient is smaller than a threshold, the edge computing processor issues an instruction to the system control module to implement fault intervention adjustment on the fuel cell stack.
In a preferred embodiment of the present invention, the edge fuel cell system operates independently and is not connected to the central computing system, and the edge computing processor computes a special feature of the fuel cell under the current control parameter, and adds the special feature into the system optimization module for optimization control, so as to implement personalized control processing of distributed edge computing, where the special feature is the operation data learned by the central computing system.
The invention solves the defects in the background art, and has the following beneficial effects:
(1) the invention provides a fuel cell system based on distributed edge calculation, which utilizes the integration, mining and learning capabilities of a central calculation system on operation data to realize maximum utilization of the data, reduces the task of overall calculation power, replaces heavy calculation power in an artificial intelligent learning fitting mode and solves the problem of insufficient calculation power of the fuel cell system.
(2) According to the invention, the purpose of integral distribution and local accurate acquisition is realized by fusing the edge battery system close to the data source with the central computing system, and the application processing capacity for calculating, sorting, learning, fitting prediction and optimizing control of the operation data of the fuel battery pack is distributed to each functional module in a balanced manner, so that the response rate and the accuracy of optimizing control of the fuel battery system are improved, and the safety of the fuel battery system is further ensured.
(3) The edge fuel cell system can be disconnected with the central computing system, namely the edge fuel cell system operates independently; under the independent operation state, the edge computing processor computes the special characteristics of the fuel cell under the current control parameters, and adds the special characteristics into the system optimization module for optimization control, thereby realizing the individualized control processing of distributed edge computing.
(4) The invention utilizes the combination of an artificial intelligence algorithm and a neural network tool in a central computing system to carry out regression learning to obtain a model of the fitting relation between the single fuel cell and the operation state of the fuel cell pack, realizes the consistency of the single fuel cell and the prediction of the operation state of the fuel cell pack under the current control parameter, and solves the problem that the output capacity of the fuel cell system is reduced because the consistency of the output voltage of the fuel cell under the current control parameter under the operation state cannot be judged according to the computing result in the prior art.
(5) The edge computing processor of the invention predicts the safety of the fuel cell set according to the learning result, and when the safety coefficient is smaller than the threshold value, the edge computing processor issues an instruction to the system control module, thereby realizing the fault intervention adjustment of the fuel cell set and further improving the safety performance of the fuel cell system.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts;
fig. 1 is a schematic diagram of a fuel cell system based on distributed edge calculation according to a preferred embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
In the description of the invention, "a plurality" means two or more unless otherwise specified.
In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the term "connected" is to be interpreted broadly, e.g. as a fixed connection, a detachable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art through specific situations.
Referring to fig. 1, a schematic diagram of a fuel cell system based on distributed edge calculation according to the present invention is shown. The distributed edge calculation is to fuse the edge battery system close to the data source with the central calculation system, realize the purposes of integral distribution and local accurate acquisition, and evenly distribute the application processing capacity of calculation, arrangement, learning, fitting prediction and optimal control of the operation data of the fuel battery pack to each functional module, thereby improving the response rate and the accuracy of the optimal control of the fuel battery system and further ensuring the safety of the fuel battery system.
The fuel cell system based on distributed edge calculation comprises: fuel cell stacks, edge fuel cell systems, and central computing systems. The fuel cell stack of the invention consists of a plurality of single fuel cells.
The edge fuel cell system collects the operation data of a plurality of fuel cells in the fuel cell group and uploads the collected operation data to the central computing system. The central computing system integrates, excavates and learns the operation data and transmits the learning result to the edge fuel cell system, and the edge fuel cell system combines the operation state of the fuel cell group and the learning result to realize the optimization control or intervention adjustment of the plurality of fuel cells. And the central computing system performs version optimization upgrading on the edge fuel cell system.
The invention utilizes the ability of the central computing system to integrate, mine and learn the operation data, realizes the maximum utilization of the data, reduces the task of overall computational power, replaces the heavy computational power by the artificial intelligent learning fitting mode, and solves the problem of insufficient computational power of the fuel cell system.
The operation data of the invention is the temperature, the air inlet flow or pressure, the opening degree signal and the voltage data of the single fuel cell of the fuel cell group under the current control parameters. Wherein the current control parameter is a parameter input by the real-time control of the fuel cell stack.
It should be noted that the optimization control here is the voltage consistency optimization control of the single fuel cell under the current control parameters. The intervention adjustment is a fault intervention adjustment performed on the fuel cell stack to predict the safety of the fuel cell stack.
The edge fuel cell system comprises an edge computer processor, a data acquisition module, a data transmission module, a system optimization module and a system control module. The edge computer has the functions of issuing collected data, transmitting data and controlling instructions to the fuel cell.
The data acquisition module receives an instruction of the edge computer processor and acquires the operation data of a plurality of fuel cells in the fuel cell pack in real time; the data transmission module receives an instruction of the edge computer processor, and the data acquisition module acquires operation data and uploads the operation data to the central computing system; the data transmission module receives the learning result and the version optimization upgrade of the central computing system and uploads the learning result and the version optimization upgrade to the edge computing processor.
The system optimization module receives the instruction of the edge computer, realizes the optimization of the fuel cells according to the running state of the cells and the learning result, and sends the optimization result to the system control module. The system optimization module optimizes the voltage consistency of the single fuel cells according to the learning result and issues the optimization result to the corresponding fuel cells through the system control module.
The system control module receives the instruction of the edge computer processor and the optimization result of the system optimization module, and sends the instruction and the optimization result to the controllers of the plurality of fuel cells to realize the voltage consistency optimization of the single fuel cells and maximize the output capacity of the fuel cell system.
The data acquisition module is provided with a plurality of monitoring units, wherein the monitoring units are a temperature monitoring unit, a throttle opening monitoring unit, a flow monitoring unit, a pressure monitoring unit and a single fuel cell voltage monitoring unit.
The central computing system comprises a data module, an intelligent learning module and a version control module. The data module realizes the receiving and sending of the operation data, integrates and manages the operation data and stores the system version of the edge fuel cell; the intelligent learning module receives the operation data of the data module, learns the operation data, realizes real-time modeling and simulation of the fuel cell stack and obtains a fitting model; and the version control module receives the learning result of the intelligent learning module, transmits the system version to the edge fuel cell system according to the learning result, and performs optimization and upgrade. The intelligent learning module is used for modeling and simulating by combining an artificial intelligence algorithm, and performing regression learning by using a neural network tool to obtain a fitting model.
The fitting model is a model of the fitting relation between the single fuel cell and the fuel cell pack running state, wherein the fitting relation is expressed by the consistency of the single fuel cell and the prediction of the fuel cell pack running state under the current control parameters.
The invention utilizes the combination of an artificial intelligence algorithm and a neural network tool in a central computing system to carry out regression learning to obtain a model of the fitting relation between the single fuel cell and the operation state of the fuel cell pack, realizes the consistency of the single fuel cell and the prediction of the operation state of the fuel cell pack under the current control parameter, and solves the problem that the output capacity of the fuel cell system is reduced because the consistency of the output voltage of the fuel cell under the current control parameter under the operation state cannot be judged according to the computing result in the prior art.
The edge computing processor of the invention predicts the safety of the fuel cell set according to the learning result, and when the safety coefficient is smaller than the threshold value, the edge computing processor issues an instruction to the system control module, thereby realizing the fault intervention adjustment of the fuel cell set and further improving the safety performance of the fuel cell system.
The edge fuel cell system of the present invention may be disconnected from the central computing system, i.e., the edge fuel cell system operates independently. Under the independent operation state, the edge computing processor computes the special characteristics of the fuel cell under the current control parameters, adds the special characteristics into the system optimization module for optimization control, and realizes the individualized control processing of distributed edge computing, wherein the special characteristics are operation data learned by the central computing system.
In light of the foregoing description of the preferred embodiment of the present invention, it is to be understood that various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (10)
1. A distributed edge computation based fuel cell system comprising: a fuel cell stack, an edge fuel cell system, and a central computing system, characterized in that,
the edge fuel cell system collects the operation data of a plurality of fuel cells in the fuel cell group and uploads the collected operation data to the central computing system;
the central computing system integrates, excavates and learns the operation data and transmits a learning result to the edge fuel cell system, and the edge fuel cell system combines the operation state of the fuel cell stack and the learning result to realize the optimization control or intervention adjustment of a plurality of fuel cells;
and the central computing system performs version optimization upgrading on the edge fuel cell system.
2. The distributed edge computing-based fuel cell system of claim 1, wherein: the edge fuel cell system comprises an edge computer processor, a data acquisition module, a data transmission module, a system optimization module and a system control module;
the edge computer has the functions of issuing acquired data, transmitting data and controlling instructions to the fuel cell;
the data acquisition module receives an instruction of the edge computer processor and acquires the operating data of a plurality of fuel cells in the fuel cell pack in real time;
the data transmission module receives an instruction of the edge computer processor, and the data acquisition module acquires the operation data and uploads the operation data to the central computing system; the data transmission module receives the learning result and the version optimization upgrade of the central computing system and uploads the learning result and the version optimization upgrade to the edge computing processor;
the system optimization module receives the instruction of the edge computer processor, realizes the optimization of a plurality of fuel cells according to the battery running state and the learning result, and sends the optimization result to the system control module;
And the system control module receives the instruction of the edge computer processor and the optimization result of the system optimization module and sends the instruction and the optimization result to the controllers of the fuel cells.
3. The distributed edge computing based fuel cell system of claim 1, wherein: the central computing system comprises a data module, an intelligent learning module and a version control module;
the data module realizes the receiving and sending of the operation data, and the data module carries out integrated management on the operation data and stores the system version of the edge fuel cell;
the intelligent learning module receives the operation data of the data module, learns the operation data, realizes real-time modeling and simulation of the fuel cell stack, and obtains a fitting model;
and the version control module receives the learning result of the intelligent learning module, transmits the system version to the edge fuel cell system according to the learning result, and performs optimization and upgrade.
4. A distributed edge computation based fuel cell system according to claim 3, wherein: the intelligent learning module is used for modeling and simulating by combining an artificial intelligence algorithm and performing regression learning by using a neural network tool to obtain a fitting model.
5. The distributed edge computing-based fuel cell system of claim 1, wherein: the operation data is data of the temperature, the intake air flow or pressure, the opening degree signal and the single fuel cell voltage of the fuel cell stack under the current control parameters.
6. The distributed edge computing-based fuel cell system of claim 2, wherein: the device comprises a data acquisition module, a pressure monitoring module and a control module, wherein the data acquisition module is internally provided with a plurality of monitoring units, and the monitoring units are a temperature monitoring unit, a throttle opening monitoring unit, a flow monitoring unit, a pressure monitoring unit and a single fuel cell voltage monitoring unit.
7. The distributed edge computing-based fuel cell system of claim 2, wherein: and the system optimization module optimizes the voltage consistency of the single fuel cell according to the learning result and issues the optimized result to the corresponding fuel cell through the system control module.
8. The distributed edge computing-based fuel cell system of claim 4, wherein: the fitting model is a model of the fitting relation of the single fuel cell and the operation state of the fuel cell stack.
9. The distributed edge computing-based fuel cell system of claim 2, wherein: and the edge computing processor predicts the safety of the fuel cell stack according to the learning result, and when the safety coefficient is smaller than a threshold value, the edge computing processor issues an instruction to the system control module to realize fault intervention adjustment of the fuel cell stack.
10. The distributed edge computing based fuel cell system of claim 1, wherein: the edge fuel cell system operates independently and is not connected with the central computing system, the edge computing processor computes special characteristics of the fuel cell under current control parameters, and adds the special characteristics into the system optimization module for optimization control, so as to realize individualized control processing of distributed edge computing, wherein the special characteristics are the operation data learned by the central computing system.
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| CN111916803A (en) * | 2020-08-10 | 2020-11-10 | 泰州市海创新能源研究院有限公司 | Fuel cell system based on cloud intelligent monitoring and edge computing |
| CN112993345A (en) * | 2021-04-21 | 2021-06-18 | 北京氢澜科技有限公司 | Artificial intelligence-based fuel cell control system and control method |
| CN114019392A (en) * | 2022-01-06 | 2022-02-08 | 苏州氢澜科技有限公司 | Single-chip voltage consistency and fault intervention system of fuel cell system |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111916803A (en) * | 2020-08-10 | 2020-11-10 | 泰州市海创新能源研究院有限公司 | Fuel cell system based on cloud intelligent monitoring and edge computing |
| CN112993345A (en) * | 2021-04-21 | 2021-06-18 | 北京氢澜科技有限公司 | Artificial intelligence-based fuel cell control system and control method |
| CN114019392A (en) * | 2022-01-06 | 2022-02-08 | 苏州氢澜科技有限公司 | Single-chip voltage consistency and fault intervention system of fuel cell system |
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