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Application of fuzzy proportion integration differentiation (PID) algorithm in fuel cell radiating system

A fuel cell and heat dissipation system technology, applied in the direction of fuel cell additives, etc., can solve the problems of steady-state error, time-varying, and strong coupling of system output and control variables, uncertain battery operating parameters, and PID control that is difficult to achieve ideal results and other problems, to achieve the effect of strong adaptability and high control precision

Inactive Publication Date: 2012-07-11
KUSN FUERSAI ENERGY
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Problems solved by technology

However, due to the time-varying, strong coupling and uncertain internal working parameters of the fuel cell system, coupled with the disturbance of external load and ambient temperature, it is difficult for the traditional PID control to achieve ideal results.
At the same time, PID control is based on error feedback control, that is, when the system is stable, there is a steady-state error between the system output and the control quantity

Method used

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  • Application of fuzzy proportion integration differentiation (PID) algorithm in fuel cell radiating system
  • Application of fuzzy proportion integration differentiation (PID) algorithm in fuel cell radiating system
  • Application of fuzzy proportion integration differentiation (PID) algorithm in fuel cell radiating system

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Embodiment Construction

[0027] like figure 1 As shown, the fuel cell heat dissipation system includes a water tank 3 connected to the fuel cell 1, the water tank 3 is connected to the radiator and the cooling fan 5 through a circulating water pump 4 and corresponding pipelines, and a main controller (FCU) 2 passes through a temperature sensor and The current sensor collects the temperature and current data signals of the fuel cell and transmits them to the fan controller 6. The fan controller 6 controls the speed of the cooling fan, that is, controls the heat dissipation. figure 1 The medium load 7 is the fuel cell power supply object, the arrow indicates the flow direction of the cooling water in the heat dissipation system, the water pump 4 is responsible for executing the water cycle, the water tank 3 is responsible for storing the cooling water, and the corresponding speed of the heat dissipation fan is obtained through the fuzzy PID control algorithm to achieve the corresponding heat dissipation...

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Abstract

The invention discloses application of a fuzzy proportion integration differentiation (PID) algorithm in a fuel cell radiating system. The fuel cell radiating system comprises a water tank connected with a fuel cell, wherein the water tank is connected with a radiator and a radiator fan through a circulating water pump and a corresponding pipeline; a main controller acquires a data signal of the fuel cell through a sensor and transmits the data signal to a fan controller; and the fan controller controls the rotating speed of the radiator fan, and obtains a corresponding rotating speed of the radiator fan through the fuzzy PID algorithm to realize corresponding radiating capacity. The application of the fuzzy PID algorithm combines fuzzy control and PID control and has the advantages of flexible fuzzy control, strong adaptability and high control accuracy.

Description

technical field [0001] The invention relates to the cooling technology of the fuel cell system, more specifically, the application of a fuzzy PID algorithm in the cooling system of the fuel cell. Background technique [0002] A fuel cell is a power generating device that converts chemical energy in fuel directly into electrical energy. The temperature control of the fuel cell system is an important link in the control of the fuel cell system, and its control effect has a great influence on the system output, system performance, and system life. In this paper, the fuel cell temperature control system is studied; fuzzy PID control and radial neural network PI control are proposed for the large inertia and large overshoot of the fuel cell cooling system. Using simulation analysis, the control effects of the two control methods are compared, and the radial Neural network PI control; after experiments, the results show that the temperature control using this algorithm has the ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H01M8/04
CPCY02E60/50
Inventor 马天才顾荣鑫金秀龙张洁王江吴伟徐加忠卞磊左琳琳
Owner KUSN FUERSAI ENERGY
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