Optimization Analysis of Hydrogen Fuel Cell Hybrid Power Based on Neural Network and Sensor Networks

Hydrogen fuel cell vehicles have concerns about clean, pollution-free, high energy utilization, and the fast fuel refueling, and they have become a hot spot in the development of new energy vehicles. Inspired by this, the paper conducts the optimization analysis of hydrogen fuel cell hybrid power based on neural network and sensor networks. In our model, in order to improve the energy efficiency of the fuel cell hybrid power system, the control of the fuel cell should be optimized according to its efficiency change characteristics, and the consumption of additional power should be minimized; at the same time, the control strategy of the fuel cell and power battery hybrid power system should be determined according to the power demand. The neurons in full connection layer are fully connected and have most of the core training parameters, hence, the neural netwotks are optimized with senser model. The proposed model is validated based on the datasets collected, where the user experience is also studied.