TY - JOUR
TI - Optimization and control of fuel cell thermal management system based on neural network
AU - Tang Kunhao
AU - Zhang Sanhua
AU - Wu Youlong
JN - Thermal Science
PY - 2021
VL - 25
IS - 4
SP - 2933
EP - 2939
PT - Article
AB - Aiming at the direct methanol fuel cell system is too complicated, difficult  to model, and the thermal management system needs to be optimized. The  article attempts to bypass the internal complexity of direct methanol fuel  cell, based on experimental data, use neural networks to approximate  arbitrarily complex non-linear functions ability to apply neural network  identification methods to direct methanol fuel cell, a highly non-linear  thermal management system optimization modelling. The paper uses 1000 sets  of battery voltage and current density experimental data as training samples  and uses an improved back propagation neural network to establish a battery  voltage-current density dynamic response model at different temperatures.  The simulation results show that this method is feasible, and the  established model has high accuracy. It makes it possible to design the  real-time controller of the direct methanol fuel cell and optimize the  thermal energy manage­ment system's efficiency.