Using Artificial Neural Network to Control the Temperature of Fuel Cell

A review of adaptive neural control applied to proton exchange membrane fuel cell systems

“…Stoichiometry (λ) Gas flow controller [33,74] Current load, purge valve [102] Difference pressure Pressure regulator [21,103] Relative humidity Boiler and pre-heater [104,105] Air blower [106] Gas flow controller [107] Thermic Temperature Gas electric heater [72] Cooling pumps [108] Gas flow controller [60] Air blower [106] Electric Power Pressure regulator [21,109] Backpressure regulator [110] types of controlled variables about the PEMFC, namely fluidic, thermic and electric, all presented in Tab.1. To be efficient in fault mitigation, a controller's response time must be consistent with the response time of the corresponding fault.…”

Thermal management system modeling of a water-cooled proton exchange membrane fuel cell

“…Some black box models based on experiments were established. Li [12] described a method using Artificial Neural Net-work (ANN) to control the temperature of the PEMFC. The model did not contain the internal mechanism of the thermal management system, which was very useful to understand the heat gain and loss.…”

Temperature Control of Proton Exchange Membrane Fuel Cell Based on Machine Learning

“…Control methods for fuel cell stack temperature control systems proposed by domestic and foreign scholars in recent years include proportional integral (PI) and state feedback control (Ahn et al, 2020;Liso et al, 2014;Zhiyu et al, 2014;Cheng et al, 2015a), Model Predictive Control (MPC) (Pohjoranta et al, 2015;Chatrattanawet et al, 2017), Fuzzy control (Wang et al, 2016;Hu et al, 2010;Cheng et al, 2015a;Ou et al, 2017), and Neural Network Control (NNC) (Li et al, 2006;Li and Li, 2006). However, the inherent nonlinearity of the PEMFC system and the uncertainty of model parameters greatly limit the effectiveness of these control methods .…”