A fuel cell system monolithic voltage consistency and fault intervention system

Abstract

The invention discloses a fuel cell system monolithic voltage consistency and fault intervention system, comprising: a data acquisition module, a data learning module, a monolithic optimization module, a fault intervention module, and a fuel cell intelligent algorithm controller; The system adopts artificial intelligence deep learning model and recursive algorithm to obtain the neural network structure for calculating fuel cell single-chip data and stack status, and uses the neural network to establish the fitting relationship between battery control parameters and running time and multiple single-chips to realize fuel cell The prediction of battery monolithic consistency and stack state solves the one-sidedness of cell voltage consistency prediction results in the prior art, effectively ensures the prediction accuracy of monolithic consistency, and improves the cell voltage consistency prediction results reliability.

Description

technical field [0001] The invention relates to the technical field of fuel cells, in particular to a fuel cell system monolithic voltage consistency and fault intervention system. Background technique [0002] Fuel cell vehicle technology is developing rapidly. As a power generation device, the fuel cell system converts chemical energy into electrical energy. Hydrogen fuel cells, as a clean energy source and environmentally friendly, have attracted widespread attention. [0003] Due to the complex control of the fuel cell system, the performance of the fuel cell is affected by many factors. A general fuel cell contains hundreds of single cells, and the quality of each single cell and the consistency between single cells determine the output performance of the entire stack. The consistency of the single cell can reflect the working state of the stack, and the variance of the single cell can be used as a very important indicator; the inconsistency between the cells is manif...

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Problems solved by technology

The system uses artificial intelligence deep learning model and recursive algorithm to obtain the neural network structure for calculating fuel cell single-chip data and stack status, and uses the neural network to establish the fitting relationship between battery control parameters and running time and multiple single-chips to achieve The prediction of fuel cell monolithic consistency and stack state solves the problem of one-sided prediction results of cell voltage consistency in the prior art

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